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Multilabel classification as a data mining task has recently attracted increasing interest from researchers. Many current data mining applications address problems with instances that belong to more than one class. These problems require the development of new, efficient methods. Advantageously using the correlation among different labels can provide better performance than methods that manage each label separately. In recent decades, many methods have been developed to deal with multilabel datasets, which makes it difficult to decide which method is the most appropriate for a given task. In this paper, we present the most comprehensive comparison carried out so far. We compare a total of 62 different methods and several configurations of each one for a total of 197 trained models. We also use a large set of problems comprising 65 datasets. In addition, we studied the efficiency of the methods considering six different classification performance metrics. Our results show that, although there are methods that repeatedly appear among the top-performing models, the best methods are closely related to the metric used for evaluating the performance. We also analyzed different aspects of the behavior of the methods. • We present a thorough comparison of multi-label methods. • The behavior depending on base classifiers is studied for many methods. • Six different metrics are used in the comparison. • Clustering of similar methods is carried out. • The behavior of the methods depending characteristics is carried out. [ABSTRACT FROM AUTHOR]

Deep learning techniques have been widely used for two-dimensional (2D) and three-dimensional (3D) computer vision problems, such as object detection, super-resolution (SR) and classification to name a few. Radar images suffer from poor resolution as compared to optical images, hence developing a high-accuracy model to solve computer vision problems, such as a classifier, is a challenge. This is because of the lack of high-frequency details in the input images which makes it difficult for the classifier model to generate accurate predictions. Ways of addressing this challenge include training the learning model with a large dataset or using a more complicated model, such as deeper layer architecture. However, employing such approaches might result in the overfitting of the model, where the model might not generalize well on previously unseen data. Also, generating a large dataset for training the model is a challenging task, especially in the case of radar images. An alternate solution for achieving high accuracy in radar classification problems is provided in this paper wherein a CNN-enabled super-resolution (SR) model is integrated with the classifier model. The SR model is trained to generate high-resolution (HR) millimeter-wave (mmW) images from any input low-resolution (LR) mmW images. These resolved images from the SR model will be used by the classifier model to classify the input images into appropriate classes, consisting of threat and non-threat objects. The training data for the dual CNN layers are generated using a numerical model of a near-field coded-aperture computational imaging (CI) system. This trained dual CNN model is tested with simulated data generated from the CI numerical model wherein a high classification accuracy of 95% and a fast inference time of 0.193 s are obtained, making it suitable for real-time automated threat classification applications. For fair comparison, the developed CNN model is also validated with experimentally generated reconstruction data, in which case, a classification accuracy of 94% is obtained. • An integrated convolutional neural network is developed which jointly performs super-resolution and classification tasks. The super-resolution part enhances the resolution of millimeter-wave input images and the classifier part predicts a class for the enhanced image. • The super-resolution part of the model enhances both the cross-range and range resolutions of the input images. In addition, the model uses both the real and imaginary parts of the reconstruction data to perform the super-resolution task. • The super-resolution and the classifier models are trained on synthesized data instead of experimentally generated data. To offer a realistic approach, additive Gaussian noise is incorporated into the synthesized data. • Validation of the integrated model with both synthesized and experimental data shows a real-time accurate super-resolution and multi-label classification performance. [ABSTRACT FROM AUTHOR]

Node classification is a popular and challenging task in graph neural networks, and existing approaches are mainly developed for a single network. With the advances in domain adaptation, researchers tend to leverage knowledge extracted from a fully-labeled source network to further improve the node classification performance in an unlabeled target network. This learning paradigm refers to cross-network node classification, which is the topic we studied in this paper. Specifically, we propose a novel model named Transferable Graph Auto-Encoders (TGAE), which first encodes the initial network data into latent representations and then decodes the learned features to preserve graph information. In the encoding phase, TGAE adopts the attentional mechanism to fuse the local and global information of nodes to discover latent node representations. To obtain transferable features between the source and target networks, TGAE aligns their distributions based on the learned representations by reducing marginal and conditional distribution differences. In the decoding phase, the latent representations are subjected to pairwise and reconstruction constraints, thus preserving structural proximity and graph topology information to learn discriminative features. Besides, a node classifier is trained to enhance the discriminant of the node representations further. Experimental results on several real-world datasets demonstrate that the proposed model achieves state-of-the-art performance in cross-network node classification tasks compared with existing methods. • TGAE is proposed to handle cross-network node classification problem. • TGAE encodes local and global information and preserves structural graph information. • Experimental results demonstrate the superior performance of the proposed model. [ABSTRACT FROM AUTHOR]

Multi-view learning has demonstrated promising performance for 3D shape recognition. However, existing multi-view methods usually focus on fusing multiple views and ignore the structural and discriminative information carried by 2D views. In this paper, we propose a multi-view hierarchical self-attention network (MHSAN) to explore the geometric and discriminative information from complex 2D views. Specifically, MHSAN consists of two self-attention networks. First, a global self-attention network is adopted to exploit the structure information by embedding position information of views. Then, the discriminative self-attention network learns discriminative information from the views with high classification scores. Through the proposed MHSAN, the geometric and discriminative information is condensed as the novel representation of 3D shapes. To validate the effectiveness of our proposed method, extensive experiments have been conducted on three 3D shape benchmarks. Experimental results demonstrate that our method is generally superior to the state-of-the-art methods in 3D shape classification and retrieval tasks. • A hierarchical self-attention network for 3D shape recognition is proposed. • The global information and the discriminative information are extracted. • Position information helps to learn the structure information of 3D object. • The views with high discriminative scores are given special attention. [ABSTRACT FROM AUTHOR]

Time series classification using phase-independent subsequences called shapelets is one of the best approaches in the state of the art. This approach is especially characterized by its interpretable property and its fast prediction time. However, given a dataset of n time series of length at most m , learning shapelets requires a computation time of O (n 2 m 4) which is too high for practical datasets. In this paper, we exploit the fact that shapelets are shared by the members of the same class to propose the SAST (Scalable and Accurate Subsequence Transform) algorithm which has a time complexity of O (n m 3). SAST is accurate, interpretable and does not learn redundant shapelets. The experiments we conducted on the UCR archive datasets showed that SAST is more accurate than the state of the art Shapelet Transform algorithm while being significantly more scalable. • We introduce the core shapelet recognition task. • We claim that time series classification by shapelets is a core shapelet recognition task. • We propose the SAST method to successfully performs the core shapelet recognition task in O (n m 3). • SAST is competive and more scalable than the SOTA shapelet methods on the UCR archive. • The implementation of our method is publicly accessible on GitHub. [ABSTRACT FROM AUTHOR]

In real applications, label noise has a great influence on data modeling. As one kind of label noise treatment method, noise filter has attracted extensive attention recently. The existing filters perform well in dealing with label noise completely at random (NCAR) but poorly in dealing with the label noise in the form of clusters (LLC). Besides, the existing filters may over remove the samples located at the classification boundaries, thereby affecting the generalization performance of classifiers. To fill these gaps, we propose a general elevating framework for label noise filters. The core idea of the framework is to improve the filtering performance of the existing filters by sample reduction. Specifically, since most of the existing filters are based on classifier prediction, and the complexity of samples at the boundary will affect the prediction performance of classifiers. Therefore, reducing the complexity of the boundary sample is very helpful to improve the performance of the filters. To this end, we propose a sample reduction method, which can not only reduce the complexity of the sample at the boundary but also convert LLC to NCAR, to get some representative samples. Next, the filter based on classifier prediction is employed to recognize the noisy representative samples. Finally, the noisy labeled samples in the given data set are found according to the identified noisy representatives. Furthermore, through empirical analysis, we found that compared with some classical metrics for evaluating the performance of noise filters, classification accuracy is more suitable to measure the performance of filters. Exhaustive experiments testify the validity of the framework, and the experimental results demonstrate that the performance of our framework is especially outstanding for LLC treatment. • The proposed noise filtering framework can alleviate the phenomenon of over-cleaning. • The performance of most existing noise filters can be improved with this framework. • The metric recommended in this paper can guide the research of the noise filter. [ABSTRACT FROM AUTHOR]

Deep Neural Networks have received considerable attention in recent years. As the complexity of network architecture increases in relation to the task complexity, it becomes harder to manually craft an optimal neural network architecture and train it to convergence. As such, Neural Architecture Search (NAS) is becoming far more prevalent within computer vision research, especially when the construction of efficient, smaller network architectures is becoming an increasingly important area of research, for which NAS is well suited. However, despite their promise, contemporary and end-to-end NAS pipeline require vast computational training resources. In this paper, we present a comprehensive overview of contemporary NAS approaches with respect to image classification, object detection, and image segmentation. We adopt consistent terminology to overcome contradictions common within existing NAS literature. Furthermore, we identify and compare current performance limitations in addition to highlighting directions for future NAS research. [ABSTRACT FROM AUTHOR]

Decision tree algorithm, because of its strong interpretability and high algorithm efficiency, is widely used in the field of pattern recognition and classification. When the number of data samples is small and there is uncertainty in the data, it is difficult for the traditional decision tree algorithm to fully mine the effective information in the data. In this paper, we use the Dempster–Shafer framework to model data uncertainty and propose a hierarchical interval estimation method to improve decision tree algorithms. The proposed method constructs intervals through two methods of attribute boundary and mean square error estimation, which not only utilizes the characteristics of intervals to model the inaccuracy of data, but also constrains intervals from two aspects, narrowing the representation range of available information. By comparing with the classic decision tree algorithm and the decision tree algorithm based on single interval estimation, the proposed method can perform classification tasks robustly and accurately in different types of data under seven data sets. • We propose an evidential decision tree based on a reliability-based BPA generation method and hierarchical interval estimation. • The proposed method greatly reduces the influence of the labels of uncertain data on the classification accuracy of the decision tree and strengthens the robustness of the algorithm. • Comparison with fifteen different decision trees, the accuracy of our proposed method on seven datasets is higher than other methods especially in the face of with uncertain attributes and labels. [ABSTRACT FROM AUTHOR]

Plenty of models have been presented to handle the hypergraph node classification. However, very few of these methods consider contrastive learning, which is popular due to its great power to represent instances. This paper makes an attempt to leverage contrastive learning to hypergraph representation learning. Specifically, we propose a novel method called Collaborative Contrastive Learning (CCL), which incorporates a generated standard graph with the hypergraph. The main technical contribution here is that we develop a collaborative contrastive schema, which performs contrast between the node views obtained from the standard graph and hypergraph in each network layer, thus making the contrast collaborative. To be precise, in the first layer, the view from the standard graph is used to augment that from the hypergraph. Then, in the next layer, the augmented features are adopted to train a new representation to augment the view from the standard graph conversely. With this setting, the learning procedure is alternated between the standard graph and hypergraph. As a result, the learning on the standard graph and hypergraph is collaborative and leads to the final informative node representation. Experimental results on several widely used datasets validate the effectiveness of the proposed model. • CCL is proposed to handle hypergraph node classification problem. • CCL considers the contrast in each layer from GCN and HGCN. • CCL exploits the convolutional networks on the standard graph and hypergraph. • Experimental results demonstrate the superior performance of the proposed model. [ABSTRACT FROM AUTHOR]

• This paper solves the problem of orthogonal linear discriminant analysis (OLDA) from the novel viewpoint of scatter matrix orthogonal decomposition. • The method can obtain approximately orthogonal sparse discriminative vectors for dimensionality reduction and jointly sparse feature extraction. • Theoretical analysis shows that OLDA can be derived by the constrained scatter matrix decomposition. • The method outperforms several well-known LDA-based and sparse learning methods on four data sets (i.e., COIL100, USPS, ICADAR2003 and CMU PIE). Orthogonal Linear Discriminant Analysis (OLDA) based on generalized Eigen-equation is widely used in the field of computer vision and pattern recognition. However, the performance of OLDA for feature extraction and classification needs to be improved as it lacks sparsity for better interpretation of the features. Moreover, computing the orthogonal sparse projections based on LDA is very difficult and is still unsolved. To solve these problems, in this paper, we propose a method called Jointly Sparse Orthogonal Linear Discriminant Analysis (JSOLDA). Different from the existing OLDA, JSOLDA is proposed from a novel viewpoint of scatter matrix decomposition. Theoretical analysis shows that OLDA can be derived by the constrained scatter matrix decomposition. In addition, by imposing L 2,1 -norm on the penalty term, the proposed JSOLDA can obtain the jointly sparse orthogonal projections to perform feature extraction. We also design an iterative algorithm to obtain the optimal solution. Systematic theoretical analysis between the OLDA and JSOLDA are uncovered. Both of convergence and computational complexity are also discussed. Experimental results on four data sets (i.e., COIL100, USPS, ICADAR2003 and CMU PIE) indicate that JSOLDA outperforms several well-known LDA-based and L 2,1 -norm based methods. [ABSTRACT FROM AUTHOR]

Highlights • Imbalance coefficient fosters measuring imbalance. • Geometric Mean and Bookmaker Informedness constitute the best unbiased metrics. • Matthews Correlation Coefficient is the best option for error consideration. • The concept of Class Balance Accuracy can be extended to other metrics. Abstract A major issue in the classification of class imbalanced datasets involves the determination of the most suitable performance metrics to be used. In previous work using several examples, it has been shown that imbalance can exert a major impact on the value and meaning of accuracy and on certain other well-known performance metrics. In this paper, our approach goes beyond simply studying case studies and develops a systematic analysis of this impact by simulating the results obtained using binary classifiers. A set of functions and numerical indicators are attained which enables the comparison of the behaviour of several performance metrics based on the binary confusion matrix when they are faced with imbalanced datasets. Throughout the paper, a new way to measure the imbalance is defined which surpasses the Imbalance Ratio used in previous studies. From the simulation results, several clusters of performance metrics have been identified that involve the use of Geometric Mean or Bookmaker Informedness as the best null-biased metrics if their focus on classification successes (dismissing the errors) presents no limitation for the specific application where they are used. However, if classification errors must also be considered, then the Matthews Correlation Coefficient arises as the best choice. Finally, a set of null-biased multi-perspective Class Balance Metrics is proposed which extends the concept of Class Balance Accuracy to other performance metrics. [ABSTRACT FROM AUTHOR]

Highlights • This paper introduces new set of moment invariants for image classification. • This paper provides a fast and accurate method for computing moment invariants. • Numerical experiments are performed to demonstrate its validity and superiority. Abstract In this paper, a new set of moment invariants, named Racah Moment Invariants (RMI), is introduced in the field of image analysis. This new set can be used to describe pattern feature independently of Rotation, Scaling and Translation transforms. Moreover, new fast and accurate algorithm, using recursive method, is developed for accelerating the computation time of the newly proposed invariants, as well as, for enhancing their numerical stability. Subsequently, several experiments have been performed. Initially, the numerical stability and computational cost are depicted. Secondly, the global and local features extraction are clearly illustrated. Then, invariability property and noise robustness are investigated. Finally, the discrimination power and the classification accuracy of the proposed invariants are extensively tested on several publicly available databases. The presented theoretical and experimental results, clearly show that the proposed method can be extremely useful in the fields of image classification. [ABSTRACT FROM AUTHOR]

• We proposed the method of constructing a fuzzy granular recurrence plot (FGRP method). • We designed an FGRP method based classification model. • The experiment shows the good performance of the FGRP method in reducing the influence of noise. • The experiment presents the good performance of the FGRP method based classification model. Recently, recurrence plot (RP) and its quantification techniques have become an important research tool in nonlinear analysis. In the existing researches, an RP is directly established on a time series ignoring the influence of noise on data, which will affect our judgement on the dynamic properties of a system. To tackle the problem there, this paper proposes a novel recurrence plot, namely fuzzy granular recurrence plot (FGRP). An FGRP of a time series is built not directly on the time series itself but on its corresponding granular time series which is composed of fuzzy information granules. With specific capability, fuzzy information granules are used as building blocks of an FGRP to achieve high-level, compact and understandable signal models. In order to apply the FGRP method to time series classification tasks, an FGRP based classification model is designed in this paper. Subsequent experiments show that the FGRP of a time series can reduce the effect of noise, and the FGRP based classification model can improve the classification performance. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose lazy bagging (LB), which builds bootstrap replicate bags based on the characteristics of test instances. Upon receiving a test instance , LB trims bootstrap bags by taking into consideration ''s nearest neighbors in the training data. Our hypothesis is that an unlabeled instance''s nearest neighbors provide valuable information to enhance local learning and generate a classifier with refined decision boundaries emphasizing the test instance''s surrounding region. In particular, by taking full advantage of ''s nearest neighbors, classifiers are able to reduce classification bias and variance when classifying . As a result, LB, which is built on these classifiers, can significantly reduce classification error, compared with the traditional bagging (TB) approach. To investigate LB''s performance, we first use carefully designed synthetic data sets to gain insight into why LB works and under which conditions it can outperform TB. We then test LB against four rival algorithms on a large suite of 35 real-world benchmark data sets using a variety of statistical tests. Empirical results confirm that LB can statistically significantly outperform alternative methods in terms of reducing classification error. [Copyright &y& Elsevier]

Abstract: Synthetic pattern generation is one of the strategies to overcome the curse of dimensionality, but it has its own drawbacks. Most of the synthetic pattern generation techniques take more time than simple classification. In this paper, we propose a new strategy to reduce the time and memory requirements by applying prototyping as an intermediate step in the synthetic pattern generation technique. Results show that through the proposed strategy, classification can be done much faster without compromising much in terms of classification accuracy, in fact for some cases it gives better accuracy in lesser time. The classification time and accuracy can be balanced according to available memory and computing power of a system to get the best possible results. [Copyright &y& Elsevier]

Recent graph neural networks (GNN) has achieved remarkable performance in node representation learning. One key factor of GNN's success is the smoothness property on node representations. Despite this, most GNN models are fragile to the perturbations on graph inputs and could learn unreliable node representations. In this paper, we study how to learn node representations against perturbations in GNN. Specifically, we consider that a node representation should remain stable under slight perturbations on the input, and node representations from different structures should be identifiable, which two are termed as the stability and identifiability on node representations, respectively. To this end, we propose a novel model called Stability-Identifiability GNN Against Perturbations (SIGNNAP) that learns reliable node representations in an unsupervised manner. SIGNNAP formalizes the stability and identifiability by a contrastive objective and preserves the smoothness with existing GNN backbones. The proposed method is a generic framework that can be equipped with many other backbone models (e.g. GCN, GraphSage and GAT). Extensive experiments on six benchmarks under both transductive and inductive learning setups of node classification demonstrate the effectiveness of our method. Codes and data are available online: https://github.com/xuChenSJTU/SIGNNAP-master-online [ABSTRACT FROM AUTHOR]

In this paper we propose a new version of penalty-based aggregation functions, the Multi Cost Aggregation choosing functions (MCAs), in which the function to minimize is constructed using a convex combination of two relaxed versions of restricted equivalence and dissimilarity functions instead of a penalty function. We additionally suggest two different alternatives to train a MCA in a supervised classification task in order to adapt the aggregation to each vector of inputs. We apply the proposed MCA in a Motor Imagery-based Brain–Computer Interface (MI-BCI) system to improve its decision making phase. We also evaluate the classical aggregation with our new aggregation procedure in two publicly available datasets. We obtain an accuracy of 82.31% for a left vs. right hand in the Clinical BCI challenge (CBCIC) dataset, and a performance of 62.43% for the four-class case in the BCI Competition IV 2a dataset compared to a 82.15% and 60.56% using the arithmetic mean. Finally, we have also tested the goodness of our proposal against other MI-BCI systems, obtaining better results than those using other decision making schemes and Deep Learning on the same datasets. • We develop relaxed versions of Restricted Equivalence Functions. • We aggregate information from different classifiers trained on different wave bands. • The new aggregations learn from the input data to discriminate between classes. • The novel aggregation functions can be adapted to the original data. [ABSTRACT FROM AUTHOR]

Long-tailed learning is attracting increasing attention due to the unbalanced distributions of real-world data. The aim is to train well-performing depth models. Traditional knowledge transfer methods for long-tailed learning are classified into feature-based horizontal knowledge transfer (HKT) and class-based vertical knowledge transfer (VKT). HKT transfers head-to-tail feature knowledge from different classes to improve classification performance when there are few tail classes. However, HKT easily leads to invalid transfer due to the deviation caused by the difference between the knowledge of head and tail classes. Fortunately, the class space has a multi-grained relationship and can form a multi-granularity knowledge graph (MGKG), which can be recast as coarse-grained and fine-grained losses to guide VKT. In this paper, we propose a hierarchical long-tailed classification method based on multi-granularity knowledge transfer (MGKT), which vertically transfers knowledge from coarse- to fine-grained classes. First, we exploit the semantic information of classes to construct an MGKG, which forms an affiliation of fine- and coarse-grained classes. Fine-grained knowledge can inherit coarse-grained knowledge to reduce transfer bias with the help of MGKG. We then propose a multi-scale feature fusion network, which aims to fully mine the rich information of the features to drive MGKT. Experiments show that the proposed model outperforms several state-of-the-art models in classifying long-tailed data. For example, our model performed 4.46% better than the next-best model on the SUN-LT dataset. • We propose a multi-scale feature fusion network about channel and spatial features. • We investigate a multi-granularity relationship of class space. • We explore a vertical transfer of coarse- to fine-grained knowledge. [ABSTRACT FROM AUTHOR]

• We propose novel graph classification method for better classification performance. • We develop an edge feature scheme and an add-on layer for discriminative feature learning. • We create corresponding graph datasets based upon ModelNet40, ModelNet10 and ShapeNet Part datasets and will release them to the community. Spectral graph convolutional neural networks (GCNNs) have been producing encouraging results in graph classification tasks. However, most spectral GCNNs utilize fixed graphs when aggregating node features while omitting edge feature learning and failing to get an optimal graph structure. Moreover, many existing graph datasets do not provide initialized edge features, further restraining the ability of learning edge features via spectral GCNNs. In this paper, we try to address this issue by designing an edge feature scheme and an add-on layer between every two stacked graph convolution layers in spectral GCNN. Both are lightweight while effective in filling the gap between edge feature learning and performance enhancement of graph classification. The edge feature scheme makes edge features adapt to node representations at different spectral graph convolution layers. The add-on layer helps adjust the edge features to an optimal graph structure. To test the effectiveness of our method, we take Euclidean positions as initial node features and extract graphs with semantic information from point cloud objects. The node features of our extracted graphs are more scalable for edge feature learning than most existing graph datasets (in one-hot encoded label format). Three new graph datasets are constructed based on ModelNet40, ModelNet10 and ShapeNet Part datasets. Experimental results show that our method outperforms state-of-the-art graph classification methods on the new datasets. Our code and the constructed graph datasets will be released to the community. [ABSTRACT FROM AUTHOR]

• A novel method CIDA, which uses the independence/dependence between features and the corresponding class/domain labels, is proposed to deal with UDA problem. • The objective function can be embedded into deep network architectures, and the whole model can be trained in an end-to-end manner. • A probabilistic interpretation of CIDA with Gaussian process is presented. • The effectiveness of CIDA is validated by several UDA benchmark datasets. Learning domain-adaptive features is important to tackle the dataset bias problem, where data distributions in the labeled source domain and the unlabeled target domain can be different. The critical issue is to identify and then reduce the redundant information including class-irrelevant and domain-specific features. In this paper, a conditional independence induced unsupervised domain adaptation (CIDA) method is proposed to tackle the challenges. It aims to find the low-dimensional and transferable feature representation of each observation, namely the latent variable in the domain-adaptive subspace. Technically, two mutual information terms are optimized at the same time. One is the mutual information between the latent variable and the class label, and the other is the mutual information between the latent variable and the domain label. Note that the key module can be approximately reformulated as a conditional independence/dependence based optimization problem, and thus, it has a probabilistic interpretation with the Gaussian process. Temporary labels of the target samples and the model parameters are alternatively optimized. The objective function can be incorporated with deep network architectures, and the algorithm is implemented iteratively in an end-to-end manner. Extensive experiments are conducted on several benchmark datasets, and the results show effectiveness of CIDA. [ABSTRACT FROM AUTHOR]

Multiple instance learning (MIL) is a form of weakly supervised learning where training instances are arranged in sets, called bags, and a label is provided for the entire bag. This formulation is gaining interest because it naturally fits various problems and allows to leverage weakly labeled data. Consequently, it has been used in diverse application fields such as computer vision and document classification. However, learning from bags raises important challenges that are unique to MIL. This paper provides a comprehensive survey of the characteristics which define and differentiate the types of MIL problems. Until now, these problem characteristics have not been formally identified and described. As a result, the variations in performance of MIL algorithms from one data set to another are difficult to explain. In this paper, MIL problem characteristics are grouped into four broad categories: the composition of the bags, the types of data distribution, the ambiguity of instance labels, and the task to be performed. Methods specialized to address each category are reviewed. Then, the extent to which these characteristics manifest themselves in key MIL application areas are described. Finally, experiments are conducted to compare the performance of 16 state-of-the-art MIL methods on selected problem characteristics. This paper provides insight on how the problem characteristics affect MIL algorithms, recommendations for future benchmarking and promising avenues for research. Code is available on-line at https://github.com/macarbonneau/MILSurvey . [ABSTRACT FROM AUTHOR]

Multi-label classification associates an unseen instance with multiple relevant labels. In recent years, a variety of methods have been proposed to handle the multi-label problems. Classifier chains is one of the most popular multi-label methods because of its efficiency and simplicity. In this paper, we consider to optimize classifier chains from the viewpoint of conditional likelihood maximization. In the proposed unified framework, classifier chains can be optimized in either or both of two aspects: label correlation modeling and multi-label feature selection. In this paper we show that previous classifier chains algorithms are specified in the unified framework. In addition, previous information theoretic multi-label feature selection algorithms are specified with different assumptions on the feature and label spaces. Based on these analyses, we propose a novel multi-label method, k -dependence classifier chains with label-specific features, and demonstrate the effectiveness of the method. [ABSTRACT FROM AUTHOR]

In this paper, we propose an innovative averaging of a set of time-series based on the Dynamic Time Warping (DTW). The DTW is widely used in data mining since it provides not only a similarity measure, but also a temporal alignment of time-series. However, its use is often restricted to the case of a pair of signals. In this paper, we propose to extend its application to a set of signals by providing an average time-series that opens a wide range of applications in data mining process. Starting with an existing well-established method called DBA (for DTW Barycenter Averaging), this paper points out its limitations and suggests an alternative based on a Constrained Dynamic Time Warping. Secondly, an innovative tolerance is added to take into account the admissible variability around the average signal. This new modeling of time-series is evaluated on a classification task applied on several datasets and results show that it outperforms state of the art methods. [ABSTRACT FROM AUTHOR]

• Discrete network embedding (DNE) is proposed for compact representation. DNE leverages hash code to represent node, and dramatically reduces computational and storage costs. • Deep discrete attributed network embedding (DDANE) is proposed to effectively leverage node attribute and network structure in attributed network. The proposed DDANE trains an improved graph convolutional network autoencoder to encode node attribute and network structure into latent discrete embedding. • Extensive experiments demonstrate the proposed methods exhibit lower storage and computational complexity than state-of-the-art network embedding methods, and achieve satisfactory accuracy. Network embedding has shown promising performance in real-world applications. The network embedding typically lies in a continuous vector space, where storage and computation costs are high, especially in large-scale applications. This paper proposes more compact representation to fulfill the gap. The proposed discrete network embedding (DNE) leverages hash code to represent node in Hamming space. The Hamming similarity between hash codes approximates the ground-truth similarity. The embedding and classifier are jointly learned to improve compactness and discrimination. The proposed multi-class classifier is further constrained to be discrete to expedite classification. In addition, this paper further extends DNE and proposes deep discrete attributed network embedding (DDANE) to learn compact deep embedding from more informative attributed network. From the perspective of generalized signal smoothing, the proposed DDANE trains an improved graph convolutional network autoencoder to effectively leverage node attribute and network structure. Extensive experiments on node classification demonstrate the proposed methods exhibit lower storage and computational complexity than state-of-the-art network embedding methods, and achieve satisfactory accuracy. [ABSTRACT FROM AUTHOR]

• We propose a big and real RGB 3D hand pose estimation dataset that contains the 2D and 3D coordinates of the joints, status of the joints occlusion, classes of different gestures, and segmentation of the 15 parts of the hand. • We propose semantic segmentation of the hand parts for estimating the weight of the occlusion at hand joints. • We exploit the occlusion information for estimating the 3D hand pose from a single RGB image. • The proposed framework is a hybrid method of classification and estimation networks to increase the validity and the accuracy of the predicted hand poses. • Classification of the hand poses is based on the different aspects including gesture, the direction of the palm, and direction of the hand. In this paper, a new framework for 3D hand pose estimation using a single RGB image is proposed. The framework is composed of two blocks. The first block formulates the hand pose estimation as a classification problem. Since the human hand can perform numerous poses, the classification network needs a huge number of parameters. So, we propose to classify hand poses based on three different aspects, including hand gesture, hand direction, and palm direction. In this way, the number of parameters will be significantly reduced. The motivation behind the classification block is that the model deals with the image as a whole and extracts global features. Furthermore, the output of the classification model is a valid pose that does not include any unexpected angle at joints. The second block estimates the 3D coordinates of the hand joints and focuses more on the details of the image pattern. RGB-based 3D hand pose estimation is an inherently ill-posed problem due to the lack of depth information in the 2D image. We propose to use the occlusion status of the hand joints to solve this problem. The occlusion status of the joints has been labeled manually. Some joints are partially occluded, and we propose to compute the extent of the occlusion by semantic segmentation. The existing methods in this field mostly used synthetic datasets. But all the models proposed in this paper are trained on more than 50 K real images. Extensive experiments on our new dataset and two other benchmark datasets show that the proposed method can achieve good performance. We also analyze the validity of the predicted poses, and the results show that the classification block increases the validity of the poses. [ABSTRACT FROM AUTHOR]

• Multimodal classification results surpasses the state-of-the-art methods. • Works with missing modalities. • Works with any kind and any number of modalities. In this paper, a graph-based, supervised classification method for multimodal data is introduced. It can be applied on data of any type consisting of any number of modalities and can also be used for the classification of datasets with missing modalities. The proposed method maps the features extracted from every modality to a space where the intrinsic structure of the multimodal data is kept. In order to map the extracted features of the different modalities into the same space and, at the same time, maintain the feature distances between similar and dissimilar modality data instances, a metric learning method is used. The proposed method has been evaluated on NUS-Wide, NTU-RGBD and AV-Letters multimodal datasets and has shown competitive results with the state-of-the-art methods in the field, while is able to cope with datasets with missing modalities. [ABSTRACT FROM AUTHOR]

• Scalar time series is unfolded to phase space to find its original structure. • Covariance matrix fuses global features, local features and their interactions. • Classification is carried out in the tangent space of statistical manifolds. Time series classification has been considered as one of the most challenging problems in data mining and widely used in a broad range of fields, such as climate, finance, medicine and computer science. The main challenges of time series classification are to select the appropriate representation (feature extraction) of time series and choose the similarity metric between time series. Compared with the traditional feature extraction method, in this paper, we focus on the fusion of global features, local features and the interaction between them, while preserving the temporal information of the local features. Based on this strategy, a highly comparative approach to univariate time series classification is introduced that uses covariance matrices as interpretable features. From the perspective of probability theory, each covariance matrix can be seen as a zero-mean Gaussian distribution. Our idea is to incorporate covariance matrix into the framework of information geometry, which is to study the geometric structures on the manifolds of the probability distributions. The space of covariance matrices is a statistical (Riemannian) manifold and the geodesic distance is introduced to measure the similarity between them. Our method is to project each distribution (covariance matrix) to a vector on the tangent space of the statistical manifold. Finally, the classification is carried out in the tangent space which is a Euclidean space. Concepts of a structural and functional network are also presented which provide us an understanding of the properties of the data set and guide further interpretation to the classifier. Experimental evaluation shows that the performance of the proposed approach exceeded some competitive methods on benchmark datasets from the UCR time series repository. [ABSTRACT FROM AUTHOR]

• The proposed model can effectively transfer knowledge from auxiliary domains, and evaluate the importance of auxiliary domains. • The proposed model can well alleviate the sparsity problem. • The proposed model can well solve the cold-start problem. • The proposed model can address the scenario of multiple auxiliary domains easily. Cross-domain collaborative filtering, which transfers rating knowledge across multiple domains, has become a new way to effectively alleviate the sparsity problem in recommender systems. Different auxiliary domains are generally different in the importance to the target domain, which is hard to evaluate using previous approaches. Besides, most recommender systems only take advantage of information from user- or item-side auxiliary domains. To overcome these drawbacks, we propose a cross-domain collaborative filtering algorithm with expanding user and item features via the latent factor space of auxiliary domains in this paper. In the proposed algorithm, the recommendation problem is first formulated as a classification problem in the target domain, which takes user and item location as the feature vector, their rating as the label. Then, Funk-SVD decomposition is employed to extract extra user and item features from user- and item-side auxiliary domains, respectively, with the purpose of expanding the two-dimensional location feature vector. Finally, a classifier is trained using the C4.5 decision tree algorithm for predicting missing ratings. The proposed algorithm can make full use of user- and item-side information. We conduct extensive experiments and compare the proposed algorithm with various state-of-the-art single- and cross-domain collaborative filtering algorithms. The experimental results show that the proposed algorithm has advantages in terms of four different evaluation metrics. [ABSTRACT FROM AUTHOR]

• A data classification method which combines phase-field with DNN is proposed. • A MNIST digital database is adopted as the experimental subjects. • The tolerance value is set to judge whether the output value in DNN is abnormal. • The proposed method performs excellent in both binary and multi-classification. • The proposed method performs better than single DNN model or phase-field model. This paper proposes a novel classification method. Firstly, we use the deep neural network (DNN) to classify the training set. After several iterations, we obtain the output vector Y. The component of the largest value in vector Y is represented as the label being classified, which we take as the output value. Because we chose the sigmoid function as our activation function, the output value is between 0 and 1. Therefore, the output value can represents the probability of the classified label by the DNN. Depending on the distribution of output values, we set tolerance values (T o l) that categorize similar output values as the same label in the DNN. If the output value is lower than T o l , we consider it categorically anomalous. Subsequently, we use the Phase-Field model to classify these anomalies and obtain better classification results. As this classification method combines Phase-Field model and DNN, we named it Phase-Field-DNN. In the numerical experiment using MNIST handwritten digit data set as experimental data, the classification accuracy of Phase-Field-DNN model is higher than that of Phase-Field model and DNN model through the analysis of the classification results of binary classification and multi-classification problems with this data. In addition, the model we proposed is used to classify the normal and abnormal brain MRIs, and the classification results are compared with those of others. After comparison, we find that our proposed model achieve the best classification results. [ABSTRACT FROM AUTHOR]

• Introducing a novel nearest neighbourhood rule for kNN ensemble. • Improving classification performance in data with diverse patterns. • Building a kNN ensemble to achieve high classification performance by making the base models diversified. • Availability of the proposed method as an R package implementation on CRAN (https://cran.r-project.org/web/packages/ExNRuleEnsemble/index.html). k NN based ensemble methods minimise the effect of outliers by identifying a set of data points in the given feature space that are nearest to an unseen observation in order to predict its response by using majority voting. The ordinary ensembles based on k NN find out the k nearest observations in a region (bounded by a sphere) based on a predefined value of k. This scenario, however, might not work in situations where the test observation follows the pattern of the closest data points with the same class that lie on a certain path not contained in the given sphere. This paper proposes a k nearest neighbour ensemble where the neighbours are determined in k steps. Starting from the first nearest observation of the test point, the algorithm identifies a single observation that is closest to the observation at the previous step. At each base learner in the ensemble, this search is extended to k steps on a random bootstrap sample with a random subset of features selected from the feature space. The final predicted class of the test point is determined by using a majority vote in the predicted classes given by all base models. This new ensemble method is applied on 20 benchmark datasets and compared with other classical methods, including k NN based models, in terms of classification accuracy, kappa and Brier score as performance metrics. Boxplots are also utilised to illustrate the difference in the results given by the proposed and other state-of-the-art methods. The proposed method outperformed the considered classical methods in the majority of cases. The proposed method is further assessed through a detailed simulation study. [ABSTRACT FROM AUTHOR]

• Review of the most commonly used distance measures in machine learning • Analysis of their main properties, applications and key aspects to consider • The similarity analysis shows a high degree of correlation between all the measures • Evaluation of classification and clustering performance, noise tolerance and runtime • Canberra distance shows the best overall performance and the highest tolerance to noise Many machine learning and data mining tasks are based on distance measures, so a large amount of literature addresses this aspect somehow. Due to the broad scope of the topic, this paper aims to provide an overview of the use of these measures in the most common machine learning problems, pointing out those aspects to consider to choose the most appropriate measure for a particular task. For this purpose, the most recent works addressing the subject were reviewed and seven of the most commonly used measures were analyzed, investigating in detail their main properties and applications. Different experiments were carried out to study their relationships and compare their performance. The degradation of the results in the presence of noise was also considered, as well as the execution time required by each measure. [ABSTRACT FROM AUTHOR]

• We propose a multilabel feature selection method that considers the trade-off between the relevance of the features and the feature cost. • We derive a score function specialized as a cost-constrained multilabel feature selection problem based on normalization. • We identify the bound of the cost factor between the relevance of the features and the importance of the feature cost. • We devise a new framework to handle the shortage of datasets containing feature costs. Feature selection is one of the key steps in building a predictive model in multi-label classification. However, most of the existing methods do not take into account information about the costs associated with considered features, such as the costs of performing diagnostic medical tests. We consider a problem of cost-constrained multilabel feature selection, which aims to select a feature subset relevant to multiple labels while satisfying a user-specific maximal admissible budget. This approach allows for building a model with high predictive power, for which the cost of making a prediction for a single instance does not exceed the user-specified budget. In this problem, the balance between the feature subset relevance and its cost should be considered concurrently, which is nontrivial in practice because their optimal balance is unknown. In this paper, we propose a novel criterion for cost-constrained multilabel feature selection that combines the relevance and cost of the candidate feature. The relevance measure is derived using the lower bound of the mutual information between the feature subset and label vector. Moreover, we propose an effective method for determining the cost-factor value that controls the trade-off between relevancy and cost. The experimental results on multilabel datasets with various characteristics demonstrate the superiority of the proposed method over conventional methods. [ABSTRACT FROM AUTHOR]

• We propose a dynamic stroke classification approach, named DyGAT, for online handwritten documents and sketches. Different from previous methods, it can make real-time prediction during writers' writing process. • DyGAT is featured by a novel graph construction algorithm, which enables graph neural networks to better exploit the contextual information under the time constraint. • To evaluate the effectiveness and generality of DyGAT, we apply it to sketch semantic segmentation, document layout analysis, and diagram recognition. Extensive experiments have been conducted to analyze our method and demonstrate its superiority. Online handwriting is widely used in human-machine interface, education, office automation, and so on. Stroke classification for online handwritten documents and sketches aims to divide strokes into several semantic categories and is a necessary step for document recognition and understanding. Previous methods are essentially static in that they have to wait for the user to finish the whole sketch before making prediction. However, in practice, the more user-friendly way is to make real-time prediction as the user is writing. In this paper, we introduce Dynamic Graph ATtention network (DyGAT) to solve the dynamic stroke classification problem. The core of our method is to formalize a document/sketch into a multi-feature graph, in which nodes represent strokes, edges represent the relationships between strokes, and multiple nodes are applied to one stroke to control the information flow. The proposed method is general and is applicable to online handwritten data of many types. We conduct experiments on popular public datasets to perform sketch semantic segmentation, document layout analysis and diagram recognition, and experimental results show competitive performance. Particularly, the proposed method achieves stroke classification accuracies which are only slightly lower than those of static classification. [ABSTRACT FROM AUTHOR]

• Proposed an effective fine-grained visual classification method based on vision transformer. • Presented a core attention aggregator to mitigate the common information loss problem existing in the transformer layer. • Designed a new key token auto-selector based on information entropy to grasp the core discriminative features. • Further extending the application of vision transformer to fine-grained visual classification. The task of fine-grained visual classification (FGVC) is to distinguish targets from subordinate classifications. Since fine-grained images have the inherent characteristic of large inter-class variances and small intra-class variances, it is considered an extremely difficult task. Most existing approaches adopt CNN-based networks as feature extractors, which causes the extracted discriminative regions to contain most parts of the object in this way, thus failing to locate the really important parts. Recently, the vision transformer (ViT) has demonstrated its power on a wide range of image tasks, which uses an attention mechanism to capture global contextual information to establish a remote dependency on the target and thus extract more powerful features. Nevertheless, the ViT model still focuses more on global coarse-grained information rather than local fine-grained information, which may lead to its undesirable performance in fine-grained image classification. To this end, we redesigned an attention aggregating transformer (AA-Trans) to better capture minor differences among images by improving the ViT structure in this paper. In detail, we propose a core attention aggregator (CAA), which enables better information sharing between each transformer layer. Besides, we further propose an innovative information entropy selector (IES) to guide the network in acquiring discriminative parts of the image precisely. Extensive experiments show that our proposed model structure can achieve a new state-of-the-art performance on several mainstream datasets. [ABSTRACT FROM AUTHOR]

As a principled method for partial correlation estimation, sparse inverse covariance estimation (SICE) has been employed to model brain connectivity networks, which holds great promise for brain disease diagnosis. For each subject, the SICE method naturally leads to a set of connectivity networks with various sparsity. However, existing methods usually select a single network from them for classification and the discriminative power of this set of networks has not been fully exploited. This paper argues that the connectivity networks at different sparsity levels present complementary connectivity patterns and therefore they should be jointly considered to achieve high classification performance. In this paper, we propose a subject-adaptive method to integrate multiple SICE networks as a unified representation for classification. The integration weight is learned adaptively for each subject in order to endow the method with the flexibility in dealing with subject variations. Furthermore, to respect the manifold geometry of SICE networks, Stein kernel is employed to embed the manifold structure into a kernel-induced feature space, which allows a linear integration of SICE networks to be designed. The optimization of the integration weight and the classification of the integrated networks are performed via a sparse representation framework. Through our method, we provide a unified and effective network representation that is transparent to the sparsity level of SICE networks, and can be readily utilized for further medical analysis. Experimental study on ADHD and ADNI data sets demonstrates that the proposed integration method achieves notable improvement of classification performance in comparison with methods using a single sparsity level of SICE networks and other commonly used integration methods, such as Multiple Kernel Learning. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new biometric pattern recognition method. In classical techniques only features of raw objects are compared. In our approach we will use composed signatures’ features. Features of a signature are associated with appropriate similarity coefficients and individually matched to a given signature. If it is necessary, composed features can be reduced. In the proposed study the most promising results are obtained from Hotelling's approach. Data comprising the composed features allow to achieve higher signature recognition level, compared to unprocessed (raw) data. It is the greatest novelty of the paper—the proposed method of data reduction together with a new type of similarity measure gives a high signature recognition level for various classes of classifiers. Leaning on investigations carried out, the classifier based on the Probabilistic Neural Network (PNN) has been introduced. Optimal parameters of the PNN have been determined by means of the Particle Swarm Optimization (PSO) procedure. The two class PNN classifier demonstrates high efficiency, compared to other classifiers. The described signature verification system consists of three units where features are captured, composed features are prepared, data are reduced and verified. The results of the study carried on signatures of the SVC2004 and MCYT databases and demonstrate the effectiveness of the proposed approach in comparison with other methods from the literature. [ABSTRACT FROM AUTHOR]

Ensemble pruning is an important task in supervised learning because of the performance and efficiency advantage it begets to predictive modelling. Performance based empirical comparison (primarily on accuracy) is the most common modus operandi for critical evaluation of ensembles pruned by different algorithms. Deep analysis of existing literature reveals that ensemble size is an ignored attribute while judging the quality of ensembles. In this paper, we argue that the cost-effectiveness of an ensemble is a function of both performance and size. Hence, equitable comparison of two ensembles must take into account both these attributes to judge their relative merits. Following this argument, we propose an objective function called accrual function which quantifies the difference in performance and size of two ensembles, to gauge their relative cost-effectiveness. The function can be parameterized and has nice mathematical properties. Semantic interpretations of these properties are delineated in the paper. Finally, we apply the accrual function on published results from selected publications and demonstrate its ability to beget clarity while comparing ensembles. [ABSTRACT FROM AUTHOR]

Highlights • We propose an end-to-end deep model with dual streams, which is able to effectively extract and make use of the global image priors and the local image features. • We incorporate a spatial pyramid convolutional transfer layer into the deep model. • We utilize multi-instance pooling layer to effectively aggregate the information contained in the feature maps. • Extensive experiments demonstrate that DELTA outperforms state-of-the-art deep multi-label classification methods. Abstract Multi-label image classification problem is one of the most important and fundamental problems in computer vision. In an image with multiple labels, the objects usually locate at various positions with different scales and poses. Moreover, some labels are associated with the entire image instead of a small region. Therefore, both the global and local information are important for classification. To effectively extract and make full use of these information, in this paper, we present a novel deep Dual-stream nEtwork for the muLTi-lAbel image classification task, DELTA for short. As its name indicates, it is composed of two streams, i.e., the Multi-Instance network and the Global Priors network. The former is used to extract the multi-scale class-related local instances features by modeling the classification problem in a multi-instance learning framework. The latter is devised to capture the global priors from the input image as the global information. These two streams are fused by the final fusion layer. In this way, DELTA can extract and make full use of both the global and local information for classification. Extensive experiments on three benchmark datasets, i.e., PASCAL VOC 2007, PASCAL VOC 2012 and Microsoft COCO, demonstrate that DELTA significantly outperforms several state-of-the-art methods. Moreover, DELTA can automatically locate the key image patterns that trigger the labels. [ABSTRACT FROM AUTHOR]

Highlights • Inter-category visual and semantic correlations are exploited. • Large numbers of structural image classes are organized hierarchically. • Hierarchical multi-task SVMs are trained over the visual-semantic tree. • The visual-semantic tree and CNNs are integrated as another framework. • We perform our experiments on 10k image categories for algorithm evaluation. Abstract Recent studies on large-scale image classification mainly focus on categorizing images into 1000 object classes, and all these 1000 object classes are atomic and mutually exclusive in the semantic space. However, for a much larger set of image categories (such as the ImageNet 10k dataset), some of them may come from the high-level (non-leaf) nodes of the concept ontology and could contain some other lower-level categories semantically. The research that classifies images into large numbers of image categories with such inter-category subsumption correlations has received rare attention. In this paper, a Visual-Semantic Tree is learned to organize 10k image categories hierarchically in a coarse-to-fine fashion, where both the inter-category visual similarities and inter-category semantic correlations are seamlessly integrated for tree construction. Additionally, a deep learning method is developed by integrating the Visual-Semantic Tree with deep CNNs to learn more discriminative tree classifiers for large-scale image classification. Our experimental results have demonstrated that the proposed Visual-Semantic Tree can effectively organize large-scale structural image categories and significantly boost the classification accuracy rates for both atomic image categories and high-level image categories. [ABSTRACT FROM AUTHOR]

Highlights • Spatial relation between object parts is considered to construct discriminative representation of fine-grained image for FGIC for the first time. • A selection strategy is proposed to find the useful spatial relations. The finegained image can be represented by exploiting the interaction between object parts. • The proposed method is evaluated on CUB-200-2011 and FGVC Aircraft datasets and the experiment results prove the effectiveness of the proposed method. Abstract Fine-Grained Image Classification (FGIC) aims to distinguish the images within a subordinate category. Recently, many FGIC methods have been proposed and huge progress has been made in the aspects of part detection and feature learning for FGIC. However, FGIC still remains a challenging task due to the large intra-class variance and small inter-class variance. To classify fine-grained images accurately, this paper proposes to exploit spatial relation to capture more discriminative details for FGIC. The proposed method contains two core modules: part selection module and representation module. The part selection module utilizes intrinsic spatial relation between object parts to select object part pairs with high discrimination power. The representation module exploits the interaction between object parts to describe the selected part pairs and construct a semantic image representation for FGIC. The proposed method is evaluated on CUB-200-2011 and FGVC-Aircraft datasets. Experimental results show that the classification accuracy of the proposed method can reach 85.5% on CUB-200-2011 and 86.9% on FGVC-Aircraft respectively, which exceed comparison methods obviously. [ABSTRACT FROM AUTHOR]

Highlights • We design a new locality-aware coding layer for CNNs. • Our model can be implemented under an end-to-end learning framework. • A powerful descriptor is obtained for texture classification. • Our approach notably outperforms the state-of-the-art methods. Abstract Recent studies have demonstrated advantages of the representations learned by Convolutional Neural Networks (CNNs) in providing an appealing paradigm for visual classification tasks. Most existing methods adopt activations from the last fully connected layer as the image representation. This paper advocates exploiting appropriately convolutional layer activations to constitute a powerful descriptor for texture classification under an end-to-end learning framework. The main component of our method is a new locality-aware coding layer conducted with the locality constraint, where the dictionary and the encoding representation are learned simultaneously. The layer is readily amenable to training via the backpropagation as the locality-aware coding process has an analytical solution. It is capable of capturing class-specific information which makes the learned convolutional features more robust. The resulting representation is particularly useful for texture classification. Comprehensive experiments on the DTD, FMD and KTH-T2b datasets show that our approach notably outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Abstract Dimensionality reduction in high dimensional multi-view datasets is an important research topic. It can keep essential features to improve performance in subsequent tasks such as classification and clustering. This paper proposes a generalized framework, which extends the PCA idea of minimizing least squares reconstruction errors, to include data distribution and multiple dictionaries for preserving outliers-free global structures in multi-view datasets. To also preserve local manifold structures, multiple local graphs are incorporated. Finally two models, in Multi-dictionary Least Squares Framework regularized with Multi-graph Embeddings (MD-MGE), are proposed for preserving both global and local structures. Extensive experimental results on four multi-view datasets prove both methods outperform the existing comparative methods. Also, their accuracy rates improvements are statistically significant on all cases below the significance level of 0.05. [ABSTRACT FROM AUTHOR]

Highlights • We propose a novel two-dimensional unsupervised feature selection model to directly conduct the feature selection on the matrix data. For sparse matrix regression under unsupervised case, the key point is to connect the matrix and its cluster label. In this work, we utilize a nonnegative spectral clustering to compute a cluster label matrix. To integrate the merits of both sparse matrix regression and nonnegative spectral clustering, we joint these two techniques as a whole optimization model for feature selection. • Our JSMRNS extends the SMR model to its unsupervised case. Although JSMRNS is the combination of sparse matrix regression and nonnegative spectral clustering, our JSMRNS can be viewed as the two-dimensional extension of the NDFS model through theoretical analysis, which ensures that our JSMRNS works. Moreover, in certain situation, JSMRNS can approximate the vector-based regression model, i.e., NDFS. • We devise an efficient optimization strategy to solve this joint sparse matrix regression and nonnegative spectral analysis optimization problem. Some theoretical discussions are presented to show the convergence behavior and computational complexity of the optimization strategy. Moreover, we give the detailed discussions to compare with the subspace learning and some relevant models. Extensive experimental results also confirm the effectiveness of our proposed method. Abstract Unsupervised feature selection is a challenging task to gain relevant features for improving learning performance due to lack of the label information. Traditional unsupervised feature selection methods are often vector-based, which may ignore the location information of original matrix element. In this paper, we propose a joint sparse matrix regression and nonnegative spectral analysis model for two-dimensional unsupervised feature selection. To obtain proper label information under unsupervised condition, we adopt a nonnegative spectral clustering technique to yield the clustering labels as the pseudo class labels. To directly select the relevant feature on matrix data, we construct a regression relationship between matrix data and the pseudo class labels by deploying left and right regression matrices. Our proposed method can integrate the merits of both sparse matrix regression and nonnegative spectral clustering for feature selection. An efficient optimization algorithm is designed to solve our proposed optimization problem. Extensive experimental results on clustering and classification demonstrate the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

Highlights • A novel time series feature selection task with labeled and unlabeled data. • A new semi-supervised time series feature learning model is proposed. • The model integrates least square minimization, spectral analysis, scaled pseudo labels as well as time series feature similarity regularization terms. • Experiments on real-world data demonstrating significant performance gain of the proposed model. Abstract Time series classification has attracted much attention in the last two decades. However, in many real-world applications, the acquisition of sufficient amounts of labeled training data is costly, while unlabeled data is usually easily to be obtained. In this paper, we study the problem of learning discriminative features (segments) from both labeled and unlabeled time series data. The discriminative segments are often referred to as shapelets. We present a new Semi-Supervised Shapelets Learning (SSSL for short) model to efficiently learn shapelets by using both labeled and unlabeled time series data. Briefly, SSSL engages both labeled and unlabeled time series data in an integrated model that considers the least squares regression, the power of the pseudo-labels, shapelets regularization, and spectral analysis. The experimental results on real-world data demonstrate the superiority of our approach over existing methods. [ABSTRACT FROM AUTHOR]

Abstract In streaming data classification, most of the existing methods assume that all arrived evolving data are completely labeled. One challenge is that some applications where only small amount of labeled examples are available for training. Incremental semi-supervised learning algorithms have been proposed for regularizing neural networks by incorporating various side information, such as pairwise constraints or user-provided labels. However, it is hard to put them into practice, especially for non-stationary environments due to the effectiveness and parameter sensitivity of such algorithms. In this paper, we propose a novel incremental semi-supervised learning framework on streaming data. Each layer of model is comprised of a generative network, a discriminant structure and the bridge. The generative network uses dynamic feature learning based on autoencoders to learn generative features from streaming data which has been demonstrated its potential in learning latent feature representations. In addition, the discriminant structure regularizes the network construction via building pairwise similarity and dissimilarity constraints. It is also used for facilitating the parameter learning of the generative network. The network and structure are integrated into a joint learning framework and bridged by enforcing the correlation of their parameters, which balances the flexible incorporation of supervision information and numerical tractability for non-stationary environments as well as explores the intrinsic data structure. Moreover, an efficient algorithm is designed to solve the proposed optimization problem and we also give an ensemble method. Particularly, when multiple layers of model are stacked, the performance is significantly boosted. Finally, to validate the effectiveness of the proposed method, extensive experiments are conducted on synthetic and real-life datasets. The experimental results demonstrate that the performance of the proposed algorithms is superior to some state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

In real-world applications, factors such as head pose variation, occlusion, and poor image quality make facial expression recognition (FER) an open challenge. In this paper, a novel conditional convolutional neural network enhanced random forest (CoNERF) is proposed for FER in unconstrained environment. Our method extracts robust deep salient features from saliency-guided facial patches to reduce the influence from various distortion types, such as illumination, occlusion, low image resolution, etc. A conditional CoNERF is devised to enhance decision trees with the capability of representation learning from transferred convolutional neural networks and to model facial expression of different perspectives with conditional probabilistic learning. In the learning process, we introduce a neurally connected split function (NCSF) as the node splitting strategy in the CoNERF. Experiments were conducted using public CK+, JAFFE, multi-view BU-3DEF and LFW datasets. Compared to the state-of-the-art methods, the proposed method achieved much improved performance and great robustness with an average accuracy of 94.09% on the multi-view BU-3DEF dataset, 99.02% on CK+ and JAFFE frontal facial datasets, and 60.9% on LFW dataset. In addition, in contrast to deep neural networks which require large-scale training data, conditional CoNERF performs well even when there are only a small amount of training data. [ABSTRACT FROM AUTHOR]

Maximum relevance and minimum redundancy (mRMR) has been well recognised as one of the best feature selection methods. This paper proposes a Kernel Partial Least Square (KPLS) based mRMR method, aiming for easy computation and improving classification accuracy for high-dimensional data. Experiments with this approach have been conducted on seven real-life datasets of varied dimensionality and number of instances, with performance measured on four different classifiers: Naive Bayes, Linear Discriminant Analysis, Random Forest and Support Vector Machine. Experimental results have exhibited the advantage of the proposed method over several competing feature selection techniques. [ABSTRACT FROM AUTHOR]

As a hot topic in machine learning, supervised learning is applied to both classification and recognition frequently. However, parameter-tuning in most supervised methods is a laborious work due to its complexity and unpredictability. In this paper, we propose an auto-weighted approach, termed as auto-weighted 2-dimensional maximum margin criterion, which updates the introduced weight in each iteration automatically to leverage the associated terms, so that the weight becomes insensitive to initialization. In addition, the proposed method extracts features from 2-order data directly, i.e., image data. Moreover, we have an observation that the objective value in the proposed method could directly reflect the performance in classification task under the varying dimensionality, which is much beneficial to selection of the optimal dimensionality. Extensive experiments on several datasets are conducted to validate that our method is of great superiority compared to other approaches. [ABSTRACT FROM AUTHOR]

In this work we describe a fast and stable algorithm for the computation of the orthogonal moments of an image. Indeed, orthogonal moments are characterized by a high discriminative power, but some of their possible formulations are characterized by a large computational complexity, which limits their real-time application. This paper describes in detail an approach based on recurrence relations, and proposes an optimized Matlab implementation of the corresponding computational procedure, aiming to solve the above limitations and put at the community’s disposal an efficient and easy to use software. In our experiments we evaluate the effectiveness of the recurrence formulation, as well as its performance for the reconstruction task, in comparison to the closed form representation, often used in the literature. The results show a sensible reduction in the computational complexity, together with a greater accuracy in reconstruction. In order to assess and compare the accuracy of the computed moments in texture analysis, we perform classification experiments on six well-known databases of texture images. Again, the recurrence formulation performs better in classification than the closed form representation. More importantly, if computed from the GLCM of the image using the proposed stable procedure, the orthogonal moments outperform in some situations some of the most diffused state-of-the-art descriptors for texture classification. [ABSTRACT FROM AUTHOR]

Markov networks and Bayesian networks are two popular models for classification. Vapnik–Chervonenkis dimension and Euclidean dimension are two measures of complexity of a class of functions, which can be used to measure classification capability of classifiers. One can use Vapnik–Chervonenkis dimension of the class of functions associated with a classifier to construct an estimate of its generalization error. In this paper, we study Vapnik–Chervonenkis dimension and Euclidean dimension of concept classes induced by discrete Markov networks and Bayesian networks. We show that these two dimensional values of the concept class induced by a discrete Markov network are identical, and the value equals dimension of the toric ideal corresponding to this Markov network as long as the toric ideal is nontrivial. Based on this result, one can compute the dimensional value in terms of a computer algebra system directly. Furthermore, for a general Bayesian network, we show that dimension of the corresponding toric ideal offers an upper bound of Euclidean dimension. In addition, we illustrate how to use Vapnik–Chervonenkis dimension to estimate generalization error in binary classification. [ABSTRACT FROM AUTHOR]