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In this paper, we introduce an efficient and effective algorithm for Graph-based Semi-Supervised Learning (GSSL). Unlike other GSSL methods, our proposed algorithm achieves efficiency by constructing a bipartite graph, which connects a small number of representative points to a large volume of raw data by capturing their underlying manifold structures. This bipartite graph, with a sparse and anti-diagonal affinity matrix which is symmetrical, serves as a low-rank approximation of the original graph. Consequently, our algorithm accelerates both the graph construction and label propagation steps. In particular, on the one hand, our algorithm computes the label propagation in closed-form, reducing its computational complexity from cubic to approximately linear with respect to the number of data points; on the other hand, our algorithm calculates the soft label matrix for unlabeled data using a closed-form solution, thereby gaining additional acceleration. Comprehensive experiments performed on six real-world datasets demonstrate the efficiency and effectiveness of our algorithm in comparison to five state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Abstract Background Computational drug repurposing is crucial for identifying candidate therapeutic medications to address the urgent need for developing treatments for newly emerging infectious diseases. The recent COVID-19 pandemic has taught us the importance of rapidly discovering candidate drugs and providing them to medical and pharmaceutical experts for further investigation. Network-based approaches can provide repurposable drugs quickly by leveraging comprehensive relationships among biological components. However, in a case of newly emerging disease, applying a repurposing methods with only pre-existing knowledge networks may prove inadequate due to the insufficiency of information flow caused by the novel nature of the disease. Methods We proposed a network-based complementary linkage method for drug repurposing to solve the lack of incoming new disease-specific information in knowledge networks. We simulate our method under the controlled repurposing scenario that we faced in the early stage of the COVID-19 pandemic. First, the disease-gene-drug multi-layered network was constructed as the backbone network by fusing comprehensive knowledge database. Then, complementary information for COVID-19, containing data on 18 comorbid diseases and 17 relevant proteins, was collected from publications or preprint servers as of May 2020. We estimated connections between the novel COVID-19 node and the backbone network to construct a complemented network. Network-based drug scoring for COVID-19 was performed by applying graph-based semi-supervised learning, and the resulting scores were used to validate prioritized drugs for population-scale electronic health records-based medication analyses. Results The backbone networks consisted of 591 diseases, 26,681 proteins, and 2,173 drug nodes based on pre-pandemic knowledge. After incorporating the 35 entities comprised of complemented information into the backbone network, drug scoring screened top 30 potential repurposable drugs for COVID-19. The prioritized drugs were subsequently analyzed in electronic health records obtained from patients in the Penn Medicine COVID-19 Registry as of October 2021 and 8 of these were found to be statistically associated with a COVID-19 phenotype. Conclusion We found that 8 of the 30 drugs identified by graph-based scoring on complemented networks as potential candidates for COVID-19 repurposing were additionally supported by real-world patient data in follow-up analyses. These results show that our network-based complementary linkage method and drug scoring algorithm are promising strategies for identifying candidate repurposable drugs when new emerging disease outbreaks.

Background: Computational drug repurposing is crucial for identifying candidate therapeutic medications to address the urgent need for developing treatments for newly emerging infectious diseases. The recent COVID-19 pandemic has taught us the importance of rapidly discovering candidate drugs and providing them to medical and pharmaceutical experts for further investigation. Network-based approaches can provide repurposable drugs quickly by leveraging comprehensive relationships among biological components. However, in a case of newly emerging disease, applying a repurposing methods with only pre-existing knowledge networks may prove inadequate due to the insufficiency of information flow caused by the novel nature of the disease. Methods: We proposed a network-based complementary linkage method for drug repurposing to solve the lack of incoming new disease-specific information in knowledge networks. We simulate our method under the controlled repurposing scenario that we faced in the early stage of the COVID-19 pandemic. First, the disease-gene-drug multi-layered network was constructed as the backbone network by fusing comprehensive knowledge database. Then, complementary information for COVID-19, containing data on 18 comorbid diseases and 17 relevant proteins, was collected from publications or preprint servers as of May 2020. We estimated connections between the novel COVID-19 node and the backbone network to construct a complemented network. Network-based drug scoring for COVID-19 was performed by applying graph-based semi-supervised learning, and the resulting scores were used to validate prioritized drugs for population-scale electronic health records-based medication analyses. Results: The backbone networks consisted of 591 diseases, 26,681 proteins, and 2,173 drug nodes based on pre-pandemic knowledge. After incorporating the 35 entities comprised of complemented information into the backbone network, drug scoring screened top 30 potential repurposable drugs for COVID-19. The prioritized drugs were subsequently analyzed in electronic health records obtained from patients in the Penn Medicine COVID-19 Registry as of October 2021 and 8 of these were found to be statistically associated with a COVID-19 phenotype. Conclusion: We found that 8 of the 30 drugs identified by graph-based scoring on complemented networks as potential candidates for COVID-19 repurposing were additionally supported by real-world patient data in follow-up analyses. These results show that our network-based complementary linkage method and drug scoring algorithm are promising strategies for identifying candidate repurposable drugs when new emerging disease outbreaks. [ABSTRACT FROM AUTHOR]

Tackling semi-supervised learning problems with graph-based methods has become a trend in recent years since graphs can represent all kinds of data and provide a suitable framework for studying continuum limits, for example, of differential operators. A popular strategy here is p-Laplacian learning, which poses a smoothness condition on the sought inference function on the set of unlabeled data. For p < ∞ continuum limits of this approach were studied using tools from Γ -convergence. For the case p = ∞ , which is referred to as Lipschitz learning, continuum limits of the related infinity Laplacian equation were studied using the concept of viscosity solutions. In this work, we prove continuum limits of Lipschitz learning using Γ -convergence. In particular, we define a sequence of functionals which approximate the largest local Lipschitz constant of a graph function and prove Γ -convergence in the L ∞ -topology to the supremum norm of the gradient as the graph becomes denser. Furthermore, we show compactness of the functionals which implies convergence of minimizers. In our analysis we allow a varying set of labeled data which converges to a general closed set in the Hausdorff distance. We apply our results to nonlinear ground states, i.e., minimizers with constrained L p -norm, and, as a by-product, prove convergence of graph distance functions to geodesic distance functions. [ABSTRACT FROM AUTHOR]

• Based on machine vision and cameras, the MCQOG algorithm is proposed to expand the idea of manufacturing process monitoring. • MCQPG uses unlabelled feature data to overcome the limitations of the lack of labelled feature data. • The inter-class reconstruction term in MCQPG is proposed to solve the problem of unbalanced distribution. • The physical consistency constraint quantitatively evaluates the physical inconsistency to improve the quality monitoring accuracy and interpretability of MCQPG. • MCQPG performs well in LDED manufacturing process monitoring and can achieve classification prediction of porosity and crack defects. Quality monitoring of the laser directed energy deposition (LDED) manufacturing process by machine vision is essential to improve the reliability and economy of the LDED manufactured parts. For monitoring algorithms, graph-based semi-supervised learning can effectively learn supervised information from labelled data without the requirement of large amounts of manually labelled data. However, traditional graph algorithm for LDED quality monitoring is limited by insufficient use of label supervised information, inadequate consideration of the imbalance data, and lack of physically meaningful explanations and constraints on the propagation process for different types of defects. In this regard, a multiconstraint quality–probability graph (MCQPG) is proposed to monitor the quality during the LDED manufacturing process. MCQPG converts the extracted multifeatures into probability distributions, finds feature sets with similar distributions to the supervised information based on quality level standards, and uses multiconstraints to satisfy few labelled feature data, imbalance data distribution and physical consistency. For physical consistency, a nonlinear defect model is developed for crack and porosity defects, and a novel defect-guided objective prediction function is proposed, resulting in the construction of a quality level standard guided physical consistency constraint term. Experimental studies on several well-known and commonly used monitoring algorithms demonstrate that the proposed MCQPG algorithm achieves 0.96 on all four evaluation metrics (accuracy, precision, recall and F1 score), validating the effectiveness of MCQPG for LDED quality monitoring. [ABSTRACT FROM AUTHOR]

Facial Beauty Prediction (FBP) is an important visual recognition problem to evaluate the attractiveness of faces according to human perception. Most existing FBP methods are based on supervised solutions using geometric or deep features. Semi-supervised learning for FBP is an almost unexplored research area. In this work, we propose a graph-based semi-supervised method in which multiple graphs are constructed to find the appropriate graph representation of the face images (with and without scores). The proposed method combines both geometric and deep feature-based graphs to produce a high-level representation of face images instead of using a single face descriptor and also improves the discriminative ability of graph-based score propagation methods. In addition to the data graph, our proposed approach fuses an additional graph adaptively built on the predicted beauty values. Experimental results on the SCUTFBP-5500 facial beauty dataset demonstrate the superiority of the proposed algorithm compared to other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Machine learning has paved its way into induction motors fault diagnosis area, where supervised learning and deep learning have been employed. However, both learning methods require a large amount of labeled data to train the model, which pose significant challenges in real life applications. To overcome this issue, in this paper, the graph-based semi-supervised learning (GSSL) is adopted to develop a fault diagnosis method for direct online induction motors due to GSSL's superior feature that only a small amount of labeled data is needed in training datasets. To evaluate its suitability, the greedy-gradient max cut (GGMC) algorithm in the GSSL family is chosen in this study, and an effective fault diagnosis approach is developed using experimental stator currents recorded in the lab for two induction motors. The developed approach can conduct binary and multiclass classifications for faults on direct online induction motors. As a critical step, curve fitting equations are developed to calculate features for untested motor loadings by using experimental data for tested motor loadings, which enables the proposed approach to remain effective under all potential motor loading conditions.

In this paper, a novel fault diagnosis method for variable frequency drive (VFD)-fed induction motors is proposed using Wavelet Packet Decomposition (WPD) and greedy-gradient max-cut (GGMC) learning algorithm. The proposed method is developed using experimental stator current data in the lab for two 0.25 HP induction motors fed by a VFD, subjected to healthy and faulty cases under various operating frequencies and motor loadings. The features are extracted from stator current signals using WPD by evaluating energy eigenvalues and feature coefficients at decomposition levels. The proposed method is validated by comparing with other graph-based semi-supervised learning (GSSL) algorithms, local and global consistency (LGC) and Gaussian field and harmonic function (GFHF). To enable fault diagnosis for untested motor operating conditions, mathematical equations to calculate features for untested cases are developed through surface fitting using features extracted from tested cases.

Multi-label learning deals with the problem which each data example can be represented by an instance and associated with a set of labels, i.e., every example can be classified into multiple classes simultaneously. Most of the existing multi-label learning methods are supervised which cannot deal with such application scenarios where manually labeling the data is very expensive and time-consuming while the unlabeled data are very cheap and easy to obtain. This paper proposes an ensemble learning method which integrates multi-label learning and graph-based semi-supervised learning into one framework. The label correlation consistency is introduced to deal with the multi-label learning. The proposed method has been evaluated on five public multi-label datasets by comparing it with state-of-the-art supervised and semi-supervised multi-label methods according to multiple evaluation metrics to confirm its effectiveness. Experimental results show that the proposed method can achieve the comparable performance compared with the state-of-the-art methods. Furthermore, it is more confident on every single predicted label. [ABSTRACT FROM AUTHOR]

Multiple-instance learning (MIL) is a weakly supervised learning paradigm in which a training dataset contains a set of labeled bags, and each bag contains multiple number of unlabeled instances. Preparation of instance-level labels is resource intensive. Being weakly supervised, MIL is sensitive to several practical issues such as noisy label information and low witness rate. A scenario of high class imbalance and low degree-of-supervision further poses additional challenges. Recent works on graph-based label propagation methods have been shown to be effective in semi-supervised setup to address such issues by propagating the label information over graph-based manifold. Application of such semi-supervised strategies for MIL framework requires the instance-level labeling. Whenever the problem setup contains the three characteristics of high class imbalance, low degree-of-supervision and weak supervision, the state-of-the-art methods of either MIL or graph-based label propagation are inadequate when applied alone. This article proposes a non-convex formulation for instance-level MIL to find the instance-level labels by combining the benefits of both MIL and graph-based label propagation methods. The proposed approach improves the performance of the classifier using density-difference- and distance-based structural smoothness assumptions in the graph structure. This article presents the comparison of the performance of the proposed method to those of several state-of-the-art base-lines in MIL. The experimental results are shown on multiple datasets from four different applications. The proposed method is compared in a total of 616 cases (14 datasets × 11 base-line models × 4 low degree-of-supervision values). The minimum f-score improvements are 15.22%, 1.14%, and 4.25% in DAP, CIR, and ACSV datasets, respectively. [ABSTRACT FROM AUTHOR]

This paper represents a semi-supervised learning framework, which integrates multi-view learning, extreme learning machine (ELM) and graph-based semi-supervised learning. The aim is to expand the scope of adaptation of non-negative sparse graph (NNSG) framework, under a multi-view condition and a non-linear relationship. The proposed multi-view learning method will be adaptive since when data is single-view the framework will degenerate into an embedded framework for NNSG framework. The proposed ELM method also will be adaptive since the number of hidden layer neuron will change with different number of input and output layer neuron. The combination of both proposed methods outperforms traditional graph-based semi-supervised learning, such as flexible manifold embedding (FME) and NNSG framework, which can not establish an affinity matrix for multi-view and can not establish a non-liner model for unknown data. Unlike traditional graph-based semi-supervised learning methods, which only can label propagation and build linear regression models for single or multi-view data, our proposed method has an obvious advantage that is applicable to any single or multi-view data, and builds linear or non-linear models. We provides extensive experiments on four public database in order to evaluate the performance of the proposed method. These experiments demonstrate significant improvement over the state-of-the-art algorithms in label propagation and processing of new data.

In this paper, a graph-based semi-supervised learning (GSSL) algorithm, greedy-gradient max cut (GGMC), based fault diagnosis method for direct online induction motors is proposed. Two identical 0.25 HP three-phase squirrel-cage induction motors under healthy, single- and multi-fault conditions were tested in the lab. Three-phase stator currents and three-dimensional vibration signals of the two motors were recorded simultaneously in each test, and used as datasets in this study. Features for machine learning are extracted from experimental stator currents and vibration data by the discrete wavelet transform (DWT). To validate the effectiveness of the proposed GGMC-based fault diagnosis method, its classification accuracy using binary classification and multiclass classification for faults of the two motors are compared with other two GSSL algorithms, local and global consistency (LGC) and Gaussian field and harmonic function (GFHF). In this study, the performance of stator currents and vibration as a monitoring signal is evaluated, it is found that stator currents perform much better than vibration signals for multiclass classification, while they both perform well for binary classification.

Graph-based semi-supervised learning (GSSL) has attracted great attention over the past decade. However, there are still several open problems: (1) how to construct a graph that effectively represents the underlying structure of data and (2) how to incorporate label information of the labeled samples into a procedure of label propagation. Our solution mainly focuses on two aspects: (1) we propose a new graph construction technique by fusing local and global structural similarity (FLGSS). Based on an initial graph structure such as K-nearest neighbors (KNN), we utilize different types of link prediction algorithms to extract local and global graph structure information. These two types of structure information are fused into a graph structure that enhances the ability to represent the data correlation. (2) By incorporating the label correlation with feature similarity of samples, we propose an extended label propagation algorithm (ELP). Through experiments on three different types of datasets, it is shown that our method outperforms other widely used graph construction methods. The extended label inference algorithm achieves better classification results than some state-of-the-art methods. The proposed FLGSS method starts from KNN graph and two link prediction algorithms are performed to construct the graph. With the time complexity analysis, we theoretically deduce that the time complexity of FLGSS is not beyond that of KNN. Meanwhile, the time complexity of ELP remains the same as that of the traditional LP algorithm.

Traditional graph-based Semi-Supervised Learning (SSL) methods usually contain two separate steps. First, constructing an affinity matrix. Second, inferring the unknown labels. While such a two-step method has been successful, it cannot take full advantage of the correlation between affinity matrix and label information. In order to address the above problem, we propose a novel graph-based SSL method. It can learn the affinity matrix and infer the unknown labels simultaneously. Moreover, feature selection with auto-weighting is introduced to extract the effective and robust features. Further, the proposed method learns the data similarity matrix by assigning the adaptive neighbors for each data point based on the local distance. We solve the unified problem via an alternative minimization algorithm. Extensive experimental results on synthetic data and benchmark data show that the proposed method consistently outperforms the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Graph-based learning methods are very useful for many machine learning approaches and classification tasks. Constructing an informative graph is one of the most important steps since a graph can significantly affect the final performance of the learning algorithms. Sparse representation is a useful tool in machine learning and pattern recognition area. Recently, it was shown that sparse graphs (sparse representation based graphs) provide a powerful approach to graph-based semi-supervised classification. In this paper, we introduce a new graph construction method that simultaneously provides a sparse graph and integrates manifold constraints on the sparse coefficients without any prior knowledge on the graph or on its similarity matrix. Furthermore, we propose an efficient solution to the optimization problem. The proposed method imposes that the sparse coding vectors of similar samples should be also similar. Different from existing graph construction methods that are based on the use of explicit constraints or a predefined graph matrix, the proposed smoothness constraints on the graph weights implicitly adapt data to the global structure of the estimated graph. A series of experiments conducted on several public image databases shows that the proposed method can outperform many state-of-the-art methods when applied to the problem of graph-based label propagation. [ABSTRACT FROM AUTHOR]

Abstract Background High-throughput sequencing data are widely collected and analyzed in the study of complex diseases in quest of improving human health. Well-studied algorithms mostly deal with single data source, and cannot fully utilize the potential of these multi-omics data sources. In order to provide a holistic understanding of human health and diseases, it is necessary to integrate multiple data sources. Several algorithms have been proposed so far, however, a comprehensive comparison of data integration algorithms for classification of binary traits is currently lacking. Results In this paper, we focus on two common classes of integration algorithms, graph-based that depict relationships with subjects denoted by nodes and relationships denoted by edges, and kernel-based that can generate a classifier in feature space. Our paper provides a comprehensive comparison of their performance in terms of various measurements of classification accuracy and computation time. Seven different integration algorithms, including graph-based semi-supervised learning, graph sharpening integration, composite association network, Bayesian network, semi-definite programming-support vector machine (SDP-SVM), relevance vector machine (RVM) and Ada-boost relevance vector machine are compared and evaluated with hypertension and two cancer data sets in our study. In general, kernel-based algorithms create more complex models and require longer computation time, but they tend to perform better than graph-based algorithms. The performance of graph-based algorithms has the advantage of being faster computationally. Conclusions The empirical results demonstrate that composite association network, relevance vector machine, and Ada-boost RVM are the better performers. We provide recommendations on how to choose an appropriate algorithm for integrating data from multiple sources.

Aspect based Sentiment Analysis is a major subarea of sentiment analysis. Many supervised and unsupervised approaches have been proposed in the past for detecting and analyzing the sentiment of aspect terms. In this paper, a graph-based semi-supervised learning approach for aspect term extraction is proposed. In this approach, every identified token in the review document is classified as aspect or non-aspect term from a small set of labeled tokens using label spreading algorithm. The k-Nearest Neighbor (kNN) for graph sparsification is employed in the proposed approach to make it more time and memory efficient. The proposed work is further extended to determine the polarity of the opinion words associated with the identified aspect terms in review sentence to generate visual aspect-based summary of review documents. The experimental study is conducted on benchmark and crawled datasets of restaurant and laptop domains with varying value of labeled instances. The results depict that the proposed approach could achieve good result in terms of Precision, Recall and Accuracy with limited availability of labeled data. [ABSTRACT FROM AUTHOR]

• A constrained sparse graph construction method is proposed. • The method does not require a predefined affinity matrix. • The proposed constraints impose edge weights smoothness. • The proposed constraints lead to a structured sparse graph. • Performance is assessed on graph-based label propagation and embedding. Sparse representation is a useful tool in machine learning and pattern recognition area. Sparse graphs (graphs constructed using sparse representation of data) proved to be very informative graphs for many learning tasks such as label propagation, embedding, and clustering. It has been shown that constructing an informative graph is one of the most important steps since it significantly affects the final performance of the post graph-based learning algorithm. In this paper, we introduce a new sparse graph construction method that integrates manifold constraints on the unknown sparse codes as a graph regularizer. These constraints seem to be a natural regularizer that was discarded in existing state-of-the art graph construction methods. This regularizer imposes constraints on the graph coefficients in the same way a locality preserving constraint imposes on data projection in non-linear manifold learning. The proposed method is termed Sparse Graph with Laplacian Smoothness (SGLS). We also propose a kernelized version of the SGLS method. A series of experimental results on several public image datasets show that the proposed methods can out-perform many state-of-the-art methods for the tasks of label propagation, nonlinear and linear embedding. [ABSTRACT FROM AUTHOR]

Abstract Data-driven graphs constitute the cornerstone of many machine learning approaches. Recently, it was shown that sparse graphs (sparse representation based graphs) provide a powerful approach to graph-based semi-supervised classification. In this paper, we introduce a new structured sparse graph that is derived by integrating manifold-type constraints on the sparse coefficients without any a priori graph or similarity matrix. Furthermore, we introduce a direct and efficient solution to the proposed optimization problem. Unlike recent sparse graph construction methods that are based on the use of hand-crafted constraints or a predefined reference similarity matrix, our constraints are directly defined on the graph weights themselves, and can provide additional information to both local and global structures of the sparse graph. Experiments conducted on several image databases show that the proposed graph can give better results than many state-of-the-art sparse graphs when applied to the problem of graph-based label propagation. [ABSTRACT FROM AUTHOR]

Graph-based semi-supervised learning is an important semi-supervised learning paradigm. Although graph-based semi-supervised learning methods have been shown to be helpful in various situations, they may adversely affect performance when using unlabeled data. In this paper, we propose a new graph-based semi-supervised learning method based on instance selection in order to reduce the chances of performance degeneration. Our basic idea is that given a set of unlabeled instances, it is not the best approach to exploit all the unlabeled instances; instead, we should exploit the unlabeled instances that are highly likely to help improve the performance, while not taking into account the ones with high risk. We develop both transductive and inductive variants of our method. Experiments on a broad range of data sets show that the chances of performance degeneration of our proposed method are much smaller than those of many state-of-the-art graph-based semi-supervised learning methods. [ABSTRACT FROM AUTHOR]

Background: High-throughput sequencing data are widely collected and analyzed in the study of complex diseases in quest of improving human health. Well-studied algorithms mostly deal with single data source, and cannot fully utilize the potential of these multi-omics data sources. In order to provide a holistic understanding of human health and diseases, it is necessary to integrate multiple data sources. Several algorithms have been proposed so far, however, a comprehensive comparison of data integration algorithms for classification of binary traits is currently lacking. Results: In this paper, we focus on two common classes of integration algorithms, graph-based that depict relationships with subjects denoted by nodes and relationships denoted by edges, and kernel-based that can generate a classifier in feature space. Our paper provides a comprehensive comparison of their performance in terms of various measurements of classification accuracy and computation time. Seven different integration algorithms, including graph-based semisupervised learning, graph sharpening integration, composite association network, Bayesian network, semi-definite programming-support vector machine (SDP-SVM), relevance vector machine (RVM) and Ada-boost relevance vector machine are compared and evaluated with hypertension and two cancer data sets in our study. In general, kernel-based algorithms create more complex models and require longer computation time, but they tend to perform better than graph-based algorithms. The performance of graph-based algorithms has the advantage of being faster computationally. Conclusions: The empirical results demonstrate that composite association network, relevance vector machine, and Ada-boost RVM are the better performers. We provide recommendations on how to choose an appropriate algorithm for integrating data from multiple sources. [ABSTRACT FROM AUTHOR]

Most of graph construction techniques assume a transductive setting in which the whole data collection is available at construction time. Addressing graph construction for inductive setting, in which data are coming sequentially, has received much less attention. For inductive settings, constructing the graph from scratch can be very time consuming. This paper introduces a generic framework that is able to make any graph construction method incremental. This framework yields an efficient and dynamic graph construction method that adds new samples (labeled or unlabeled) to a previously constructed graph. As a case study, we use the recently proposed Two Phase Weighted Regularized Least Square (TPWRLS) graph construction method. The paper has two main contributions. First, we use the TPWRLS coding scheme to represent new sample(s) with respect to an existing database. The representative coefficients are then used to update the graph affinity matrix. The proposed method not only appends the new samples to the graph but also updates the whole graph structure by discovering which nodes are affected by the introduction of new samples and by updating their edge weights. The second contribution of the article is the application of the proposed framework to the problem of graph-based label propagation using multiple observations for vision-based recognition tasks. Experiments on several image databases show that, without any significant loss in the accuracy of the final classification, the proposed dynamic graph construction is more efficient than the batch graph construction. [ABSTRACT FROM AUTHOR]

Indoor positioning based on the received signal strength (RSS) of the WiFi signal has become the most popular solution for indoor localization. In order to realize the rapid deployment of indoor localization systems, solutions based on crowdsourcing have been proposed. However, compared to conventional methods, lots of different devices are used in crowdsourcing system and less RSS values are collected by each device. Therefore, the crowdsourced RSS values are more erroneous and can result in significant localization errors. In order to eliminate the signal strength variations across diverse devices, the Linear Regression (LR) algorithm is proposed to solve the device diversity problem in crowdsourcing system. After obtaining the uniform RSS values, a graph-based semi-supervised learning (G-SSL) method is used to exploit the correlation between the RSS values at nearby locations to estimate an optimal RSS value at each location. As a result, the negative effect of the erroneous measurements could be mitigated. Since the AP locations need to be known in G-SSL algorithm, the Compressed Sensing (CS) method is applied to precisely estimate the location of the APs. Based on the location of the APs and a simple signal propagation model, the RSS difference between different locations is calculated and used as an additional constraint to improve the performance of G-SSL. Furthermore, to exploit the sparsity of the weights used in the G-SSL, we use the CS method to reconstruct these weights more accurately and make a further improvement on the performance of the G-SSL. Experimental results show improved results in terms of the smoothness of the radio map and the localization accuracy. [ABSTRACT FROM AUTHOR]

Graph-based semi-supervised classification is widely used because it effectively exploits the characteristics of unlabeled data. However, the existing methods have a drawback in that they do not account for the inherent noise of the data. Noise in graph data refers to nodes that are isolated from classes and overlapping nodes between different classes. Therefore, neglecting noise can distort the data manifold, leading to over-smoothing and overall performance degradation. In this paper, we propose a noise-robust model called the dynamic shaving label propagation algorithm. Our proposed method comprises three parts: graph construction, noise definition and cutting, and label propagation. First, in the graph construction process, k is determined at the point when reverse nearest neighbors are identified for the most isolated nodes. Second, the noise definition process identifies the nodes classified as noise based on the number and distance of their reverse nearest neighbors. Finally, label propagation is performed dynamically and iteratively by adjusting the noise removal intensity. Using various simulated and real-world datasets, we evaluate the accuracy and noise robustness of the proposed method with those of existing methods to evaluate its effectiveness and applicability. The comparison results demonstrate that the proposed method outperforms the existing alternatives. • Label propagation is a representative graph-based semi-supervised method. • Reverse nearest neighbor information is used to build a k -nearest neighbors graph. • Considering noise can alleviate oversmoothing problem. • Properly defining and shaving noise improves the performance of noisy data. [ABSTRACT FROM AUTHOR]

The high cost of data labeling often results in node label shortage in real applications. To improve node classification accuracy, graph-based semi-supervised learning leverages the ample unlabeled nodes to train together with the scarce available labeled nodes. However, most existing methods require the information of all nodes, including those to be predicted, during model training, which is not practical for dynamic graphs with newly added nodes. To address this issue, an adversarially regularized graph attention model is proposed to classify newly added nodes in a partially labeled graph. An attention-based aggregator is designed to generate the representation of a node by aggregating information from its neighboring nodes, thus naturally generalizing to previously unseen nodes. In addition, adversarial training is employed to improve the model's robustness and generalization ability by enforcing node representations to match a prior distribution. Experiments on real-world datasets demonstrate the effectiveness of the proposed method in comparison with the state-of-the-art methods. The code is available at https://github.com/JiarenX/AGAIN. • Inductive learning on partially labeled attributed graphs is addressed. • An adversarially regularized graph attention model is proposed. • Experiments on real-world datasets demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Semi-supervised Discriminant Embedding (SDE) is the semi-supervised extension of Local Discriminant Embedding (LDE). Since this type of methods is in general dealing with high dimensional data, the small-sample-size (SSS) problem very often occurs. This problem occurs when the number of available samples is less than the sample dimension. The classic solution to this problem is to reduce the dimension of the original data so that the reduced number of features is less than the number of samples. This can be achieved by using Principle Component Analysis for example. Thus, SDE needs either a dimensionality reduction or an explicit matrix regularization, with the shortcomings both techniques may suffer from. In this paper, we propose an exponential version of SDE (ESDE). In addition to overcoming the SSS problem, the latter emphasizes the discrimination property by enlarging distances between samples that belong to different classes. The experiments made on seven benchmark datasets show the superiority of our method over SDE and many state-of-the-art semi-supervised embedding methods. [ABSTRACT FROM AUTHOR]

While the advantages of using information from multi-source domains for establishing an adaptation model have been widely recognized, how to boosting the robustness of the learning model has only recently received attention. For achieving a robust adaptation learning model for visual classification tasks, by exploiting multi-source adaptation regularization joint kernel sparse representation (ARJKSR), we propose a novel M ulti-source A daptation learning method with G lobal and L ocal R egularization (MAGLR), which aims to minimize a cost function that properly trades off the global and local learning costs so as to utilize both the local and global discriminative information of target domain. Specifically, we first learn the optimal ARJKSR of domain datasets, which jointly represents the datasets by a sparse linear combination of a self-expressive dictionary in some optimal reproduced kernel Hilbert space (RKHS) recovered by minimizing the inter-domain distribution discrepancy, whilst constraining the observations from domains to share their sparse representations. Thus, we simultaneously take into account correlations as well as coupling information among multiple source domains. Based on the ARJKSR, we then derive a robust label prediction matrix for unlabeled instances from the target domain based on the classical graph-based semi-supervised learning diagram, into which global and local regularization are incorporated. Furthermore, a multi-kernel regression way is exploited to extend MAGLR to out-of-sample data. The validity of our method is examined by several real-world visual classification tasks. Experimental results demonstrate the superiority of our method. [ABSTRACT FROM AUTHOR]

Most of the existing domain adaptation learning (DAL) methods relies on a single source domain to learn a classifier with well-generalized performance for the target domain of interest, which may lead to the so-called negative transfer problem. To this end, many multi-source adaptation methods have been proposed. While the advantages of using multi-source domains of information for establishing an adaptation model have been widely recognized, how to boost the robustness of the computational model for multi-source adaptation learning has only recently received attention. To address this issue for achieving enhanced performance, we propose in this paper a novel algorithm called multi-source A daptation R egularization J oint K ernel S parse R epresentation (ARJKSR) for robust visual classification problems. Specifically, ARJKSR jointly represents target dataset by a sparse linear combination of training data of each source domain in some optimal Reproduced Kernel Hilbert Space (RKHS), recovered by simultaneously minimizing the inter-domain distribution discrepancy and maximizing the local consistency, whilst constraining the observations from both target and source domains to share their sparse representations. The optimization problem of ARJKSR can be solved using an efficient alternative direction method. Under the framework ARJKSR, we further learn a robust label prediction matrix for the unlabeled instances of target domain based on the classical graph-based semi-supervised learning (GSSL) diagram, into which multiple Laplacian graphs constructed with the ARJKSR are incorporated. The validity of our method is examined by several visual classification problems. Results demonstrate the superiority of our method in comparison to several state-of-the-arts. [ABSTRACT FROM AUTHOR]