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In this article, a semisupervised weighting method for feature dimension based on entropy is proposed for classification, dimension reduction, and correlation analysis. For real-world data, different feature dimensions usually show different importance. Generally, data in the same class are supposed to be similar, so their entropy should be small; and those in different classes are supposed to be dissimilar, so their entropy should be large. According to this, we propose a way to construct the weights of feature dimensions with the whole entropy and the innerclass entropies. The weights indicate the contribution of their corresponding feature dimensions in classification. They can be used to improve the performance of classification by giving a weighted distance metric and can be applied to dimension reduction and correlation analysis as well. Some numerical experiments are given to test the proposed method by comparing it with some other representative methods. They demonstrate that the proposed method is feasible and efficient in classification, dimension reduction, and correlation analysis.

In general, vehicle images have varying resolutions due to vehicles’ movements and different camera settings. However, most existing vehicle re-identification models are single-resolution deep networks trained with pre-uniformly resizing vehicle images, which underestimates adverse effects of varying resolutions and leads to unsatisfactory performance. A straightforward solution for dealing with varying resolutions is to train multiple vehicle re-identification models. Each model is independently trained with images of a specific resolution. However, this straightforward solution requires significant overhead and ignores intrinsic associations among different resolution images. For that, an efficient multi-resolution network (EMRN) is proposed for vehicle re-identification in this paper. Firstly, EMRN embeds a newly-designed multi-resolution feature dimension uniform module (MR-FDUM) behind a traditional backbone network (i.e., ResNet-50). As a result, the whole model can extract fixed dimensional features from different resolution images so that it can be trained with one loss function of fixed dimensional parameters rather than training multiple models. Secondly, a multi-resolution image randomly feeding strategy is designed to train EMRN, making each mini-batch data of a random resolution during the training process. Consequently, EMRN can implicitly learn collaborative multi-resolution features via only a unitary deep network. Experiments on three large scale datasets, i.e., VeRi776, VehicleID, and VRIC, demonstrate that EMRN is superior to state-of-the-art vehicle re-identification methods.

Touch modality identification has attracted increasing attention due to its importance in human-robot interactions. There are three issues involved in the tactile perception for the touch modality identification, including the high dimensionality of tactile signals, complex tensor morphology of tactile sensing units, and the misalignment among different tactile time-series samples. In this article, we propose a novel kernel-based approach to deal with these three issues in a unified framework. Specifically, the techniques, including sparse principal component analysis and subsampling, are employed to reduce the feature dimension. Then, a singular value decomposition (SVD)-based kernel is proposed to preserve the spatial information of the tactile sensing elements. The sample misalignment issue is addressed via the employment of a global alignment kernel. Moreover, the merits of these two kernels are fused through an ideal regularized composite kernel, which simultaneously takes the label information of the training set into consideration. The effectiveness of the proposed kernel-based approach is verified on a public touch modality data set with a comprehensive comparison with the competing methods.

With the growing needs of practical applications such as security monitoring, partial face recognition is a challenging but important issue, because the captured faces in real-world surveillance videos may be occluded or with variations. Though current face recognition methods perform well in relatively constrained scenes, they may suffer from degradation for partial faces. In this paper, we propose a framework of model-based transfer learning and sparse coding (MTLSC) for partial face recognition. First, due to less information in partial face image, we exploit the mirrored image of an original probe sample as sample augment to provide further information. Considering the inadequacy of training face samples, we obtain face features based on model-based transfer learning VGGNet that is pre-trained on VGGFace dataset. Then we reconstruct face features by sliding window in view of different sizes of partial face hard to extract the same feature dimension. Finally we carry out sparse coding with rectification and calculate the minimum score of the probe and mirrored samples among all classes to get the results. Thus, by model-based transfer learning, sliding window for feature reconstruction and sparse coding with rectification, the proposed framework improves partial face recognition performance. Experimental results on three face databases (LFW, AR and NIR), and two person re-identification databases (iLIDS-VID and PKU-Reid) demonstrate our method is effective for partial face recognition.

Text non-text separation is one of the most essential pre-processing steps for any optical character recognition (OCR) system. As an OCR engine can only process texts, the non-texts present in an input document image are required to be suppressed at the initial level. Therefore, to build a complete OCR system, an efficient text non-text separation module needs to be developed. To this end, we have proposed a texture-based feature descriptor followed by a novel feature selection technique for region-based text non-text classification. First, we have incorporated rotation invariant property with local ternary pattern to form a new texture-based feature descriptor, rotation invariant local ternary pattern (RILTP). Next, a novel feature selection technique is proposed which is a modified version of binary particle swarm optimization (BPSO). For the evaluation of the proposed text non-text classification method, we have initially constructed a database consisting of 690 images of text and non-text regions extracted from 70 pages of RDCL 2015 and 75 pages of RDCL 2017 page segmentation competitions databases. In this database, each class contains 345 data samples. The proposed texture-based feature descriptor has obtained an accuracy of 97.09% on this database. Whereas, after applying BPSO, the feature dimension is reduced by approximately 55% and at the same time, the accuracy reaches 97.5%. Furthermore, in this work, another database is also created from Media team document pages to validate the robustness of this method. The second database comprises 100 text and 100 non-text images. The method has achieved 96.28% accuracy when it is trained with the first database and tested with the second database. The comparative study reveals the robustness and strength of the proposed method as it outnumbers many state-of-the-art texture-based features. Besides, the proposed feature selection method is also compared with various standard feature selection methods, and it has been observed that the proposed one outperforms all those methods considered here for comparison.

The linear separability effect refers to a benefit in search performance observed in a feature-search task, where target and distractor features vary along a continuous feature dimension: Search performance is best when there is a boundary in feature space that separates the distractor features from the target feature. However, the role that distractor heterogeneity plays in this effect is not well understood. Here, we reexamined this effect in the context of a new predictive procedure from Lleras et al. (2019) that quantifies the impact of distractor heterogeneity on search performance. Experiments 1A and 1B measured people's performance in homogeneous search conditions where they searched for the target among one type of distractor. The parameters observed in Experiments 1A and B were then used to predict search times in Experiments 2 and 3, where the target was presented in heterogeneous displays containing two types of distractors. The results show that total variance accounted for was 95% to 98%, without including any factor indexing the linear separability rule. The results demonstrate that heterogeneous search in orientation space is a function of target-distractor similarity and interitem interactions. The study highlights the robustness of the predictive procedure and demonstrates the generalizability of the method to estimate interitem interactions to new stimulus types. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Detection of dysphonia is useful for monitoring the progression of phonatory impairment for patients with Parkinson’s disease (PD), and also helps assess the disease severity. This paper describes the statistical pattern analysis methods to study different vocal measurements of sustained phonations. The feature dimension reduction procedure was implemented by using the sequential forward selection (SFS) and kernel principal component analysis (KPCA) methods. Four selected vocal measures were projected by the KPCA onto the bivariate feature space, in which the class-conditional feature densities can be approximated with the nonparametric kernel density estimation technique. In the vocal pattern classification experiments, Fisher’s linear discriminant analysis (FLDA) was applied to perform the linear classification of voice records for healthy control subjects and PD patients, and the maximum a posteriori (MAP) decision rule and support vector machine (SVM) with radial basis function kernels were employed for the nonlinear classification tasks. Based on the KPCA-mapped feature densities, the MAP classifier successfully distinguished 91.8% voice records, with a sensitivity rate of 0.986, a specificity rate of 0.708, and an area value of 0.94 under the receiver operating characteristic (ROC) curve. The diagnostic performance provided by the MAP classifier was superior to those of the FLDA and SVM classifiers. In addition, the classification results indicated that gender is insensitive to dysphonia detection, and the sustained phonations of PD patients with minimal functional disability are more difficult to be correctly identified.

Previous approaches to texture analysis and segmentation use multichannel filtering by applying a set of filters in the frequency domain or a set of masks in the spatial domain. This paper presents two new texture segmentation algorithms based on multichannel filtering in conjunction with neural networks for feature extraction and segmentation. The features extracted by Gabor filters have been applied for image segmentation and analysis. Suitable choices of filter parameters and filter bank coverage in the frequency domain to optimize the filters are discussed. Here we introduce two methods to optimize Gabor filter bank. First, a Gabor filter bank with a flat response is implemented and the optimal feature dimension is extracted by competitive networks. Second, a subset of Gabor filter bank is selected to compose the best discriminative filters, so that each filter in this small set can discriminate a pair of textures in a given image. In both approaches, multilayer perceptrons are employed to segment the extracted features. The comparisons of segmentation results generated using the proposed methods and previous research using Gabor, discrete cosine transform (DCT), and Laws filters are presented. Finally, the segmentation results generated by applying the optimized filter banks to textured images are presented and discussed.

In the field of pattern recognition, the problem of offline character recognition has long been a fascinating challenge. Several studies demonstrate that the process of image recognition is a little computationally expensive. The objective of this study includes an effective and reliable technique for the offline identification of Latin font characters produced by computers with a less number feature dimension. For which an effective representation technique for the character images is needed. The framework uses some machine learning techniques, SVM, random forest, RNN, and CNN, such as a standard recognition classifier. This study uses four feature extraction technologies to assess the efficiency of the proposed framework on a particular Chars74kFnt dataset. The extracted feature set originally introduced here is of 528 dimensions. Results show that CNNs deep neural network classifier outperforms the others without compromising efficiency. Later on, this dimension is reduced from 528 to just 220 by applying the Maximum Relevance Minimum Redundancy (mRMR) feature selection process. The results of these tests are shown in separate tables and also analyzed.

This work focuses on training difficulty and high computational cost caused by high-resolution input image when car plate character recognition based on support vector machine is applied. The use of singular value decomposition technology is proposed with improved optimal threshold analysis strategy to select retained features. The original data set is mapped in the low-dimensional feature space and the model is trained. The same test set is used to evaluate the models trained under different feature dimensions, and the optimal feature dimension is determined to 32 dimensional features. Results showed that the algorithm can accurately classify and detect license plate characters. The proposed model achieves 99.69% accuracy of the original data set, demonstrates excellent advantages, and remarkably simplifies the model design when only 96 dimensional features are used compared with traditional linear and convolutional neural network classifiers. New methods reduce the algorithm run time by 200%, and the number of parameter storage required by the algorithm to 25%. Hence, the proposed method can significantly improve the license plate recognition system usage in resource-constrained embedded devices.

Accurate precipitation prediction is very important for social life and economical activity. Prediction of the quantitative precipitation in semi-arid areas is difficult because of rain scarcity and volatility. In this study, the 3-h precipitation situation in the semi-arid region of Lanzhou is predicted, that is, the precipitation status after 3 h is forecasted on 5 levels: ‘no rain’, ‘light rain’, ‘moderate rain’, ‘heavy rain’ and ‘torrential rain’. We selected the meteorological data from 25 stations in and nearby Lanzhou, and processed the data with lag, difference and multiplication. Due to the large number of features, we use Mutual Information (MI) feature extraction method to reduce feature dimension, extract the features that are highly correlated with the target variable, and introduce spatio-temporal information in this way. Precipitation in semi-arid areas also has the problem of sample imbalance. We oversampled the data using Adaptive Synthetic (ADASYN) sampling approach and generated some minority class samples. Based on the MI feature extraction method and the ADASYN oversampling method, we constructed an Adaptive Synthesis and Mutual Information extraction Matrix (ASMI-M), which is the feature matrix used for model training. Then we proposed a Convolutional Neural Network and Long Short Term Memory (CNN-LSTM) model based on deep learning to predict the 3-h precipitation in Lanzhou City, which has achieved better prediction performance than traditional machine learning methods.

Three-dimensional (3D) shape recognition is a popular topic and has potential application value in the field of computer vision. With the recent proliferation of deep learning, various deep learning models have achieved state-of-the-art performance. Among them, multiview-based 3D shape representation has received increased attention in recent years, and related approaches have shown significant improvement in 3D shape recognition. However, these methods focus on feature learning based on the design of the network and ignore the correlation among views. In this article, we propose a novel progressive feature guide learning network (PGNet) that focuses on the correlation among multiple views and integrates multiple modalities for 3D shape recognition. In particular, we propose two information fusion schemes from visual and feature aspects. The visual fusion scheme focuses on the view level and employs the soft-attention model to define the weights of views for visual information fusion. The feature fusion scheme focuses on the feature dimension information and employs the quantified feature as the mask to further optimize the feature. These two schemes jointly construct a PGNet for 3D shape representation. The classic ModelNet40 and ShapeNetCore55 datasets are applied to demonstrate the performance of our approach. The corresponding experiment also demonstrates the superiority of our approach.

Spoken language identification (LID) is the process of determining and classifying natural language from a given content and dataset. Data must be processed to extract useful features to perform LID. The mel-frequency cepstral coefficient (MFCC) is one of the most popular feature extraction techniques in LID. The MFCC features are generated to serve as inputs for the classification stage. In this study, reduction in the MFCC feature dimension is investigated because large data size affects the computational time and resources (i.e., memory space) and slows the identification speed. The implementation of data reduction techniques to retain the most important feature parameters is also evaluated in this study. The investigation of data reduction is based on standard deviation (STD) calculation and principal component analysis (PCA). The features based on MFCC and the reduced dimensions based on STD and PCA results are then used as inputs to an optimized extreme learning machine (ELM) classifier called the optimized genetic algorithm-ELM (OGA-ELM). Several sets of data samples with one dimension of principal components (i.e., 119) are utilized for the evaluation. The results are generated using two different datasets. The first dataset is derived from eight separate languages, whereas the second dataset is a part of the National Institute of Standards and Technology Language Recognition Evaluation 2009 dataset. To evaluate the performance of the proposed method, this study utilizes several assessment measures, namely, accuracy, recall, precision, F-measure, G-mean, and identification time. The best LID performance is observed when the MFCC based on STD and PCA features with 119 feature dimensions is used with OGA-ELM as the classifier. The experimental results show that the proposed MFCC method achieves 99.38% accuracy using the first dataset. Additionally, it achieves accuracies of up to 97.60%, 96.80%, and 91.20% using the second dataset with durations of 30, 10, and 3 s, respectively. The proposed MFCC method exhibits the fastest computational time in all experiments, requiring only a few seconds to identify languages. Using a data reduction technique can substantially speed up the computational time, overcome resource limitations, and improve LID performance.

Accurate classification of Alzheimer’s disease (AD) and mild cognitive impairment (MCI), especially distinguishing the progressive MCI (pMCI) from stable MCI (sMCI), will be helpful in both reducing the risk of converting into AD and also releasing the burden on the family and even the society. In this study, a novel deep belief network (DBN) based multi-task learning algorithm is developed for the classification issue. In particular, the dropout technology and zero-masking strategy are exploited for getting over the overfitting problem and also enhancing the generalization ability and robustness of the model. Then, a new framework based on the DBN-based multi-task learning is established for accurate diagnosis of AD. After MRI preprocessing, not only the principal component analysis is utilized to reduce the feature dimension, but also multi-task feature selection approach is introduced to select the feature set related to all tasks as a result of taking the internal relevancy among multiple related tasks into consideration. Using data from the ADNI dataset, our method achieves satisfactory results in six tasks of health control (HC) vs. AD, HC vs. pMCI, HC vs. sMCI, pMCI vs. AD, sMCI vs. AD and sMCI vs. pMCI with the accuracies are 98.62%, 96.67%, 92.31%, 91.89%, 99.62% and 87.78%, respectively. Experimental results demonstrate that the DBN-based MTL algorithm developed in this study is an effective, superior and practical method of AD diagnosis.

The current mainstream methods of loop closure detection in visual simultaneous localization and mapping (SLAM) are based on bag-of-words (BoW). However, traditional BoW-based approaches are strongly affected by changes in the appearance of the scene, which leads to poor robustness and low precision. In order to improve the precision and robustness of loop closure detection, a novel approach based on stacked assorted auto-encoder (SAAE) is proposed. The traditional stacked auto-encoder is made up of multiple layers of the same autoencoder. Compared with the visual BoW model, although it can better extract the features of the scene image, the output feature dimension is high. The proposed SAAE is composed of multiple layers of denoising auto-encoder, convolutional auto-encoder and sparse auto-encoder, it uses denoising auto-encoder to improve the robustness of image features, convolutional auto-encoder to preserve the spatial information of the image, and sparse auto-encoder to reduce the dimensionality of image features. It is capable of extracting low to high dimensional features of the scene image and preserving the spatial local characteristics of the image, which makes the output features more robust. The performance of SAAE is evaluated by a comparison study using data from new college dataset and city centre dataset. The methodology proposed in this paper can effectively improve the precision and robustness of loop closure detection in visual SLAM.

The pathophysiology of major depressive disorder (MDD) has been explored to be highly associated with the dysfunctional integration of brain networks. It is therefore imperative to explore neuroimaging biomarkers to aid diagnosis and treatment. In this study, we developed a spatiotemporal graph convolutional network (STGCN) framework to learn discriminative features from functional connectivity for automatic diagnosis and treatment response prediction of MDD. Briefly, dynamic functional networks were first obtained from the resting‐state fMRI with the sliding temporal window method. Secondly, a novel STGCN approach was proposed by introducing the modules of spatial graph attention convolution (SGAC) and temporal fusion. A novel SGAC was proposed to improve the feature learning ability and special anatomy prior guided pooling was developed to enable the feature dimension reduction. A temporal fusion module was proposed to capture the dynamic features of functional connectivity between adjacent sliding windows. Finally, the STGCN proposed approach was utilized to the tasks of diagnosis and antidepressant treatment response prediction for MDD. Performances of the framework were comprehensively examined with large cohorts of clinical data, which demonstrated its effectiveness in classifying MDD patients and predicting the treatment response. The sound performance suggests the potential of the STGCN for the clinical use in diagnosis and treatment prediction., In this study, we developed a spatiotemporal graph convolutional network framework to learn discriminative features from functional connectivity for automatic diagnosis and treatment response prediction of major depressive disorder. Performances of the framework were comprehensively examined with large cohorts of clinical data, which demonstrated its effectiveness in classifying major depressive disorder patients and predicting the treatment response.

In 1993, Krishnapuram and Keller proposed possibilistic c -means (PCM) clustering, where the PCM had various extensions in the literature. However, the PCM algorithm with its extensions treats data points under equal importance for features. In real applications, different features in a dataset should take different importance with different weights. In this article, we first propose a feature-weighted PCM (FW-PCM). We then construct a feature-reduction framework. Therefore, we give a feature-weighted possibilistic c -means clustering with a feature-reduction framework, termed as a feature-weighted reduction PCM (FW-R-PCM) algorithm. The proposed FW-R-PCM can improve the clustering performance of PCM by calculating feature weights to identify important features, and so it can consequently eliminate these irrelevant features to reduce feature dimension. Its theoretical behavior and computational complexity are also analyzed. The effectiveness and usefulness of FW-R-PCM are demonstrated through experimental results using synthetic and real datasets, where comparisons of FW-R-PCM with PCM, FW-PCM, and some existing feature-weighted clustering algorithms are also made.

Cancer is one of the deadly diseases prevailing worldwide and the patients with cancer are rescued only when the cancer is detected at the very early stage. Early detection of cancer is essential as, in the final stage, the chance of survival is limited. The symptoms of cancers are rigorous and therefore, all the symptoms should be studied properly before the diagnosis. Thus, an automatic prediction system is necessary for classifying cancer as malignant or benign. Hence, this paper introduces the novel strategy based on the JayaAnt lion optimization-based Deep recurrent neural network (JayaALO-based DeepRNN) for cancer classification. The steps followed in the developed model are data normalization, data transformation, feature dimension detection, and classification. The first step is data normalization. The goal of data normalization is to eliminate data redundancy and to mitigate the storage of objects in a relational database that maintains the same information in several places. After that, the data transformation is carried out based on log transformation that generates the patterns using more interpretable and helps fulfill the supposition, and to reduce skew. Also, the non-negative matrix factorization is employed for reducing the feature dimension. Finally, the proposed JayaALO-based DeepRNN method effectively classifies cancer based on the reduced dimension features to produce a satisfactory result. Thus, the resulted output of the proposed JayaALO-based DeepRNN is employed for cancer classification. The proposed JayaALO-based DeepRNN showed improved results with maximal accuracy of 95.97%, maximal sensitivity of 95.95%, and maximal specificity of 96.96%.

Deep learning has been successfully applied in process monitoring in recent years due to its powerful feature extraction. However, these monitoring methods are difficult to extract intrinsic representations of the process data in complex nonlinear processes. A new deep neural network, residual attention convolutional autoencoder (RACAE) is proposed for process monitoring. The unsupervised learning method of RACAE can extract representative features from high-dimensional data, which can significantly improve process monitoring performance in nonlinear processes. RACAE effectively integrates convolution calculation with an autoencoder to perform effective feature extraction of multivariate data. Moreover, residual attention block is embedded in the autoencoder to select these key features and then reduce the feature dimension for detector. A new process monitoring model is proposed and two kinds of statistics are developed for fault detection. The effectiveness of RACAE in fault detection is evaluated through a numerical case and two benchmark processes. The convolutional autoencoder based on residual attention provides a new approach for feature learning and process monitoring of complex processes.

Hyperspectral images (HSIs) include hundreds of spectral bands, which lead to Hughes phenomenon in classification task and decrease the classification accuracy. Feature selection can remove redundant and noisy features in the HSIs to overcome this phenomenon. In real applications, we may face a HSI scene with only a few labeled samples. Meanwhile, there are adequate labeled samples in a similar HSI scene. For example, they share the same land-cover classes. The shared information can be used to help the scene with a few labeled samples in feature selection. Traditional single-scene-based feature selection appears powerless in solving such problems. Cross-scene feature selection provides an attractive way to select feature subsets by simultaneously using the information from two HSI scenes. However, spectral distribution may change due to atmospheric conditions, yielding spectral shift. In order to tackle this problem, we propose a cross-domain algorithm based on hybrid whale optimization algorithm with simulated annealing (WOASA). The newly proposed algorithm is dubbed cross-domain WOASA (CDWOASA). CDWOASA simultaneously considers the separability of different land-cover classes and the consistency of selected features between two scenes, leading to discriminative and domain-invariant characters of selected feature subset. Moreover, since the original WOASA is not able to precisely control the dimension of selected features, we propose an improvement using a sorting strategy based on the fitness function value, thus making the output feature dimension precisely controlled. The experimental results on two cross-scene HSI datasets demonstrate the superiority of CDWOASA in cross-scene feature selection.

In multi-modal learning tasks such as video understanding, the most important operations are feature extraction, feature enhancement for single modality and feature aggregation between modalities. In this paper, we present two attention based algorithms, the Position-embedding Non-local (PE-NL) Network and the Multi-modal Attention (MA) feature aggregation method. Inspired by Non-local Neural Networks and Transformers, our PE-NL is a self-attention liked feature enhancement operation and it can capture long-range dependencies and model relative positions. The MA aggregation method merges visual and audio modals while reduces feature dimension and the number of parameters without losing too much accuracy. Both of PE-NL and MA blocks can be plugged into many multi-modal learning architectures. Our Gated PE-NL-MA network achieves competitive results on Youtube-8M dataset.

In recent years, many researchers have explored different methods to obtain discriminative features for electroencephalogram-based (EEG-based) emotion recognition, but a few studies have been investigated on deaf subjects. In this study, we have established a deaf EEG emotion dataset, which contains three kinds of emotion (positive, neutral, and negative) with 15 subjects. Ten kinds of time–frequency domain features and eleven kinds of nonlinear dynamic system features were extracted from the EEG signals. To obtain the optimal feature combination and optimal classifier, an integrated genetic firefly algorithm (IGFA) was proposed. The multi-objective function with variable weight was utilized to balance the classification accuracy and the feature reduction ratio that are contradictory goals to find brighter fireflies in each generation. To retain the historical optimal solution and reduce the feature dimension, an optimal population protection scheme and subgroups generation scheme was carried out. The experimental results show that the averaged feature reduction rate of the proposed method is 0.959, and the averaged classification accuracy is 0.961. By investigating important brain regions, deaf subjects have common areas in the frontal and temporal lobes for EEG emotion recognition, while individual areas occur in the occipital and parietal lobes.

When a compound fault occurs, the randomness and ambiguity of the gearbox will cause uncertainty in the collected signal and reduce the accuracy of signal feature extraction. To improve accuracy, this research proposes a gearbox compound fault feature extraction method, which uses the inverse cloud model to obtain the signal feature value. First, EEMD is used to decompose the collected vibration signals of gearbox faults in normal and fault states. Then, the mutual information method is used to select the sensitive eigenmode function that can reflect the characteristics of the signal. Subsequently, the inverse cloud generator is used to extract cloud digital features and construct sample feature sets. On this basis, the concept of synthetic cloud is introduced, and the cloud-based distance measurement principle is used to synthesize new clouds, reduce the feature dimension, and extract relevant features. Finally, a simulation experiment on a rotating machinery unit with a certain type of equipment verifies that the proposed method can effectively extract the feature of gearbox multiple faults with less feature dimension. And comparing with the feature set extracted by the single cloud model, the results show that the method can better represent the fault characteristic information of the signal.

Face spoofing detection, also known as liveness detection, is a challenging and one of the most active research areas in computer vision. In this paper, a novel texture descriptor, namely equilibrium difference local binary pattern (ED-LBP), is proposed for the representation and recognition of face texture. First, the adjacent pixels discrepancy in a facial image is fully considered and the discrepancy information is encoded into LBP. This novel texture feature extraction method has the advantage of getting more elaborate texture information without increasing the feature dimension. Second, the texture signatures in different color channels are fully investigated. More specifically, the feature histograms are calculated over each image band separately. Third, by the integration of the chromatic ED-LBP histograms and the two-level spatial pyramid, the local structure information of face is encoded in our approach which can well describe the differences between facial videos of valid users and impostors. Finally, the ED-LBP histograms from different color spaces are usually cascaded into a united feature vector which is feed into SVM for classification identification. Extensive experiments on four challenging and publicly available face anti-spoofing databases, namely CASIA FASD, Replay-Attack, Replay-Mobile, and OULU-NPU, demonstrate the effectiveness of our proposed approach. The results indicate that our methods are superior to state-of-the-art techniques and can effectively resist photo and video spoofing attacks in face recognition (FR).

With the rapid development of the Internet, various forms of network attack have emerged, so how to detect abnormal behavior effectively and to recognize their attack categories accurately have become an important research subject in the field of cyberspace security. Recently, many hot machine learning-based approaches are applied in the Intrusion Detection System (IDS) to construct a data-driven model. The methods are beneficial to reduce the time and cost of manual detection. However, the real-time network data contain an ocean of redundant terms and noises, and some existing intrusion detection technologies have lower accuracy and inadequate ability of feature extraction. In order to solve the above problems, this paper proposes an intrusion detection method based on the Decision Tree-Recursive Feature Elimination (DT-RFE) feature in ensemble learning. We firstly propose a data processing method by the Decision Tree-Based Recursive Elimination Algorithm to select features and to reduce the feature dimension. This method eliminates the redundant and uncorrelated data from the dataset to achieve better resource utilization and to reduce time complexity. In this paper, we use the Stacking ensemble learning algorithm by combining Decision Tree (DT) with Recursive Feature Elimination (RFE) methods. Finally, a series of comparison experiments by cross-validation on the KDD CUP 99 and NSL-KDD datasets indicate that the DT-RFE and Stacking-based approach can better improve the performance of the IDS, and the accuracy for all kinds of features is higher than 99%, except in the case of U2R accuracy, which is 98%.

Wearable electroencephalography sensor integrated with seizure detection algorithm has become an emerging research topic in health management for patients with refractory epilepsy. To enhance seizure detection accuracy and reduce power consumption, this paper has proposed a selection method of optimal features customized for each patient. A feature pool consisting of several popular features is constructed. Then, the customized multi-domain features for each patient are selected from feature pool based on minimum redundancy maximum relevance. By removing redundant features, the computational complexity of on-line seizure detection can be greatly reduced, which is desired in wearable device where low-power and real-time operation are required. Four test datasets are used to evaluate the performance of proposed method, with detection accuracy calculated. Experiment results show that different datasets vary in optimal features. By using the proposed method, the detection accuracy and the value of area under receiver operating characteristic curve can be improved by 11.49% and 0.13 respectively, while feature dimension being reduced to 1.

Text non-text classification is an important research problem in the domain of document image processing. Undesirably, this is an almost ignored research topic, particularly, when we consider the unconstrained offline handwritten document images. For text non-text classification, many times researchers employ high dimensional feature vectors, which not only increase the computation time and storage requirement, but also reduce the classification accuracy due to the presence of redundant or irrelevant features. Here lies the application of some feature selection (FS) algorithms in order to find out the relevant subset of the features from the original feature vector. In this paper, our aim is two-fold. Firstly, application of coalition game based FS technique to find out an optimal feature subset for classifying the components present in a handwritten document image either as text or non-text. Secondly, five variants of a popular texture based feature descriptor, called Local Binary Pattern (LBP), along with its basic version are fed to the FS module for identifying the useful patterns only which can pinpoint the regions of an image as most informative in terms of the said classification task. To the best of our knowledge, the approach is completely novel where coalition game based FS technique is applied for locating the feature-rich regions to be used for text non-text classification. For experimentation, we have prepared an in-house dataset along with its ground truth information which consists of 104 handwritten engineering class notes as well as laboratory copies that include handwritten and printed texts, graphical components and tables etc. Experimental outcomes confirm that the proposed approach not only helps in reducing the feature dimension significantly but also increases the recognition ability of all six feature vectors.

Automatic classification of glaucoma from fundus images is a vital diagnostic tool for Computer-Aided Diagnosis System (CAD). In this work, a novel fused feature extraction technique and ensemble classifier fusion is proposed for diagnosis of glaucoma. The proposed method comprises of three stages. Initially, the fundus images are subjected to preprocessing followed by feature extraction and feature fusion by Intra-Class and Extra-Class Discriminative Correlation Analysis (IEDCA). The feature fusion approach eliminates between-class correlation while retaining sufficient Feature Dimension (FD) for Correlation Analysis (CA). The fused features are then fed to the classifiers namely Support Vector Machine (SVM), Random Forest (RF) and K-Nearest Neighbor (KNN) for classification individually. Finally, Classifier fusion is also designed which combines the decision of the ensemble of classifiers based on Consensus-based Combining Method (CCM). CCM based Classifier fusion adjusts the weights iteratively after comparing the outputs of all the classifiers. The proposed fusion classifier provides a better improvement in accuracy and convergence when compared to the individual algorithms. A classification accuracy of 99.2% is accomplished by the two-level hybrid fusion approach. The method is evaluated on the public datasets High Resolution Fundus (HRF) and DRIVE datasets with cross dataset validation.

To make full use of the key information in frame-level features, a DNN-based model for speech enhancement is proposed using self-attention on the feature dimension. Two improvement strategies are adopted to strengthen the attention of the fully connected layers to the effective information in the features. First, the model introduces the fusion of feature domains on the input features, using a 136-dimensional combination of features including the MFCC, AMS, RASTA-PLP, cochleagram, and PNCC. The fusion of features complements information from different domains, including the mel domain and gammatone domain, thus providing more effective information for self-attention. Second, a feature-level self-attention mechanism is applied to the output of the fully connected layer to obtain the information related to the task. The feature-level attention enables the fully connected layers to capture the internal correlations between different features and to reduce the redundancy brought by multiple features. The experimental results show that under the matched noisy condition, compared to the noisy signals, the proposed algorithm increased the PESQ, fwsegSNR and STOI by 40.92%, 60.2% and 8.31%, respectively, and by 23.64%, 32.55% and 3.4%, respectively, under the mismatched noisy condition. Comparisons between different neural networks indicate that the proposed algorithm is superior to the compared algorithms in both the matched and mismatched situations using fewer context frames. Therefore, the proposed model can effectively utilize the key information of the features to suppress the noise, thereby improving the speech quality and generalizing the mismatched samples.

Handwritten music symbol recognition is considered by the research fraternity as a critical research problem. It becomes more critical when the symbols are collected from handwritten music sheets in offline mode. Most of the research findings, available in the literature, have tried to recognize the said symbols using various shape based features. But this approach limits system performance when we dealt with lookalike symbols such as half note, eight note and quarter note. To encounter this, in the present work we have used a texture based feature descriptor, called Daisy, for the said purpose. Though Daisy descriptor yields reasonably good recognition accuracy, but it generates a high dimensional feature vector. Hence, in this work, Quantum concept inspired Grey Wolf Optimization, named as QGWO, has been applied to select optimal feature subset from this high dimensional feature vector. We have applied the proposed method on six different standard music symbol datasets that include HOMUS, Capitan_score_uniform, Capitan_score_non-uniform, Fornes, Rebelo_real and Rebelo_synthetic datasets. On these datasets we have achieved recognition accuracies 93.07%, 99.22%, 99.20%, 99.49% and 100.00% respectively with 39.63%, 49.75%, 42.50%, 67.62%, 54.37% and 71.25% of actual feature dimension (i.e., 800) respectively. Additionally, we have compared our results with some state-of-the-art methods along with two recent deep learning based models, and it has been found that the present approach outperforms those.

The severe stage of Diabetic Retinopathy (DR) is characterized by the growth of new blood vessels which is called Neovascularization (NV). The abnormally grown blood vessels on the disc are breakable in nature thus the patient is at high risk of sudden blindness. Therefore, the significance of early and accurate detection of Neovascularization on Disc (NVD) should not be neglected. This paper presents an automatic detection of the optic disc using a Controlled Differential Evolution (CDE) algorithm. Further, the Region of Interest (ROI) is created automatically by extending the extreme boundaries of the optic disc by 100 pixels to ensure the presence of NV around the optic disc also. From the ROI so created, blood vessels are segmented using multi-scale Gabor filtering and subsequently, both the morphological and textural features are extracted. Simultaneously, statistical features are directly extracted from the earlier created ROI. Finally, the fundus image is classified by a Support Vector Machine (SVM) using the extracted features from all three feature sets. From each individual image, 16 features are extracted and the feature dimension is reduced to 13 using a sequential backward feature (SBF) selection algorithm. The optimal features are obtained from a total of 205 fundus images, which consists of 99 NVD positive and 106 NVD negative images. This paper attains an average accuracy of 98.75%, the specificity of 100%, the sensitivity of 97.8%, and area under the curve (AUC) as 100% when tested over image selected randomly.

Breakout is a catastrophic accident in continuous casting. The existing breakout prediction methods based on logical judgment and neural networks need to constantly adjust the prediction parameters or prepare high-quality samples as the input, resulting in poor robustness and unstable precision stability. Therefore, it is particularly important to develop a breakout prediction method that not only can predict breakout accurately but also avoid human intervention significantly. This work proposes a novel approach for breakout prediction combining K-means clustering and feature dimension reduction. The method uses feature dimension reduction to obtain the typical feature vector (TFV) that can characterize the original temperature change trend, and then a K-means clustering model is established to realize online detection of breakout prediction. The results show that the model has a 100% alarm rate for the true breakout, and meanwhile, reduces the number of false alarms from 555 to 217 compared with the on-line breakout prediction system (BPS). The proposed method does not need to adjust the prediction parameters frequently or prepare the input samples carefully, which not only avoids the human intervention but also meets the requirements of online monitoring for the practicality and applicability of the breakout prediction method.

It has been a popular trend to decode individuals' demographic and cognitive variables based on MRI. Features extracted from MRI data are usually of high dimensionality, and dimensionality reduction (DR) is an effective way to deal with these high-dimensional features. Despite many supervised DR techniques for classification purposes, there is still a lack of supervised DR techniques for regression purposes. In this study, we advanced a novel supervised DR technique for regression purposes, namely, supervised multidimensional scaling (SMDS). The implementation of SMDS includes two steps: (1) evaluating pairwise distances among entities based on their labels and constructing a new space through a distance-preserving projection; (2) establishing an explicit linear relationship between the feature space and the new space. Based on this linear relationship, DR for test entities can be performed. We evaluated the performance of SMDS first on a synthetic dataset, and the results indicate that (1) SMDS is relatively robust to Gaussian noise existing in the features and labels; (2) the dimensionality of the new space exerts negligible influences upon SMDS; and (3) when the sample size is small, the performance of SMDS deteriorates with the increase of feature dimension. When applied to features extracted from resting state fMRI data for individual age predictions, SMDS was observed to outperform classic DR techniques, including principal component analysis, locally linear embedding and multidimensional scaling (MDS). Hopefully, SMDS can be widely used in studies on MRI-based predictions. Furthermore, novel supervised DR techniques for regression purposes can easily be developed by replacing MDS with other nonlinear DR techniques.

Computer-aided diagnosis of epilepsy based on Electroencephalography (EEG) analysis is a beneficial practice which adopts machine learning to increase the recognition rate and saves physicians from long hours of EEG inspection. However multi-channel epilepsy EEG signals reflect significant nonlinearity with different degrees of cross-talk among channels, which further leads to high dimensional features extracted from EEG. These shortcomings make the performance of epilepsy detection with machine learning difficult to improve. In order to get fast and accurate detection performance, a feature dimension reduction algorithm based on epilepsy locality preserving projections (E-LPP) is proposed. E-LPP, by preserving the low-dimensional manifold as much as possible, enables to analyze signals of non-linear, non-stationary and high-dimensional nature. To get the best performance, we determine the hyperparameters of E-LPP by grid search. Subsequently a fusion epilepsy detection framework combined feature extraction with E-LPP is proposed to classify whether subjects’ seizure onset or not. We test our method on two well-known and widely studied datasets which includes ictal and interictal EEG recordings. The experimental result on recall, precision and F1 is superior to the common traditional dimensionality reduction algorithm, manifold learning algorithm and autoencoder based deep learning, which indicates this proposed method not only makes it possible to solve nonlinearity and cross-talk among channels in EEG, but also tackles the inherent difficulties regarding unbalanced epilepsy data with high metrics.

In the research of computer-aided diagnosis, the shortage of disease feature dimension curse and the imbalance of medical samples have always been the focus of research on diagnostic decision support systems. For these two problems, we propose a feature selection algorithm based on association rules and an integrated classification algorithm based on random equilibrium sampling. We extracted and cleaned the electronic medical record text obtained from the hospital to obtain a diabetes data set. The proposed algorithm was verified in this data set and the public data set UCI. Experimental results show that the feature selection algorithm based on association rules is better than the CART, ReliefF and RFE-SVM algorithms in terms of feature dimension and classification accuracy. The proposed integrated classification algorithm based on random equalization sampling is superior to the comparative SMOTE-Boost and SMOTE-RF algorithms in macro precision, macro-full rate and macro F1 value, which embodies the robustness of the algorithm.

Feature selection is an effective method to reduce the feature dimension and improve the efficiency of data mining algorithms, it can be used to solve the ”curse of dimensionality” problem. Most existing feature selection methods do not adequately consider the effects of noise and outliers, thereby reducing the performance of regression. The paper proposed a new feature selection model and supervised learning method, using self-paced regularizer (SP-regularizer) terms and l2, 1-norm to constrain the model. Specifically, the feature selection model is trained firstly using the most high-confidence samples base on the self-paced learning theory, and then adds the more high-confidence training samples in the remaining samples to increase the generalization ability of the initial feature selection model until the generalization ability of the feature selection model is not improved or all training samples are used up. Then use l2, 1-norm remove redundant feature to improve algorithm efficiency. Experimental results on nine datasets show that our proposed method is superior to the comparison algorithms.

Improving diagnostic efficiency and shortening diagnostic time is important for improving the reliability and safety of rotating machinery, and has received more and more attention. When using intelligent diagnostic methods to diagnose bearing faults, the increasingly complex working conditions and the huge amount of data make it a great challenge to diagnose fault quickly and effectively. In this paper, a novel fault diagnosis method based on sparse auto-encoder (SAE), combined with compression sensing (CS) and wavelet packet energy entropy (WPEE) for feature dimension reduction is proposed. Firstly, vibration signals of each fault type are projected linearly through compressed sensing to obtain compressed signals, which are merged into a low-dimensional compressed signal matrix of multiple fault types. Secondly, the WPEE of low-dimensional compressed signal matrix of multi-fault type is determined, and the eigenvector matrix of bearing fault diagnosis is formed, which greatly reduces the dimension of the eigenvector matrix. Finally, SAE are constructed by adding sparse penalty to auto-encoder (AE) for high-level feature learning and bearing fault classification, and it not only further learns the high-level features of data, but also reduces the feature dimension. Compared with traditional feature extraction methods and the standard deep learning method, the proposed method not only guarantees high accuracy, but also greatly reduces the diagnosis time.

Online learning represents an important family of efficient and scalable algorithms for large-scale classification problems. Many of them are linear with fast computational speed, but when faced with complex classification, they more likely have low accuracies. In order to improve accuracies, kernel trick is applied, however, it often brings high computational cost. In fact, discriminative information is vital in classification which is still not fully utilized in these algorithms. In this paper, we proposed a novel online linear method, called Sketch Discriminatively Regularized Online Gradient Descent Classification (SDROGD). In order to exploit inter-class separability and intra-class compactness, SDROGD utilizes a matrix to characterize the discriminative information and embeds it directly into a new regularization term. This matrix can be updated by the sketch technique in an online manner. After applying a simple but effective optimization, we show that SDROGD has a good time complexity bound, which is linear with the feature dimension or the number of samples. Experimental results on both toy and real-world datasets demonstrate that SDROGD has not only faster computational speed but also much better classification accuracies than some related kernelized algorithms.

Transfer learning aims to leverage valuable information in one domain to promote the learning tasks in the other domain. Some recent studies indicated that the latent information, which has a close relationship with the high-level concepts, are more suitable for cross-domain text classification than learning raw features. To obtain more latent information existing in the latent feature space, some previous methods constructed multiple latent feature spaces. However, those methods ignored that the latent information of different latent spaces may lack the relevance for promoting the adaptability of transfer learning models,even may lead to negative knowledge transfer when there exists a glaring discrepancy among the different latent spaces. Additionally, since those methods learn the latent space distributions using a strategy of direct promotion,their computational complexity increases exponentially as the number of latent spaces increases.To tackle this challenge, this paper proposes a Multiple Groups Transfer Learning (MGTL) method. MGTL first constructs multiple different latent feature spaces and then integrates the adjacent ones that have a similar latent feature dimension into one latent space group. Along this way, multiple latent space groups can be obtained. To enhance the relevance among these latent space groups, MGTL makes the adjacent groups contain one same latent space at least. Then, different groups will have more relevance than raw latent spaces. Second, MGTL utilizes an indirect-promotion strategy to connect different latent space groups.The computational complexity of MGTL increases linearly as the number of latent space groups increases and is superior to those multiple latent space methods based on direct-promotion. In addition, an iterative algorithm is proposed to solve the optimization problem. Finally, a set of systematic experiments demonstrate that MGTL outperforms all the compared existing methods. Refereed/Peer-reviewed

The invention discloses a sequential image prediction method based on LSTM and DCGAN, and the method combines the excellent feature capture capability of the DCGAN with the LSTM, can enable the predicted image data to be visualized, and facilitates the direct observation. The improved LSTM network has convolution characteristics inside, and two-dimensional spatial characteristics of image data canbe directly learned; In order to reduce the internal learning complexity, a traditional input image is changed into an input feature; Characteristics are extracted from DCGAN, and compared with an original image, the method has the advantages that the dimension is greatly simplified, and the whole network is controllable. According to the method, the feature dimension is well reduced through theDCGAN, and the problem that the high dimension cannot be calculated is solved; The improved LSTM can better learn time sequence characteristics, so that more accurate prediction is realized; The wholenetwork structure complies with a stack type cascading strategy in the connection method, and guarantees are provided for controlling the network depth. The sequential image prediction model architecture provided by the invention is theoretically suitable for all sequential images.

To improve the generalization ability of speech enhancement algorithms for unlabeled noisy speech, a speech enhancement transfer learning model based on the feature-attention multi-kernel maximum mean discrepancy (FA-MK-MMD) is proposed. To obtain a representation of the shared subspace (the part related with clean speech in the feature extracted by shared encoder) between source domain (speech with known noise and labels) and target domain (speech with unknown noise and no labels), the algorithm takes MK-MMD as loss function for reducing distribution differences between these two domains, which could improve the adaptability to the unknown noise. Furthermore, considering that different noise have different influence on the representation of shared subspace, the attention mechanism is applied to feature dimension to screen out the information less polluted by noise, which is helpful for reconstructing the clean speech. In the term of speech with unknown noise and no labels, the experiments demonstrate that the proposed algorithm has improved the frequency-weighed segmental signal-to-noise ratio (fwsegSNR), the perceptual evaluation of the speech quality (PESQ) and the short time objective intelligibility (STOI) compared with the baseline algorithm.

Neural networks lately have achieved a great success on sentiment classification due to their ability of feature extraction. However, it remains as an enormous challenge to model long texts in document-level sentiment classification as well as to explore semantics between sentences and dependencies between features. Moreover, most existing methods can hardly differentiate the importance of different contents when constructing a document representation. To tackle these problems, we propose a novel neural network model: AttDR-2DCNN, which mainly consists of two parts. The first part utilizes a two-layer compositional bidirectional Gated Recurrent Unit (GRU) to obtain the compositional semantics of the document, where the first layer learns the feature vector of the sentence, and the second layer learns the document matrix representation consisting of two dimensions of the time-step dimension and feature dimension, from the sentence vectors. The second part applies a two-dimensional convolution operation and two-dimensional max pooling to capture more dependencies between sentences features. We further utilize different types of attention mechanism in these two parts to distinguish the importance of words, sentences and features in the document. Experiments are conducted on four publicly available document-level review datasets and the result shows that the proposed model outperforms some existing models.

In this paper gait-based method which correlates age with gender information is investigated to estimate human age-group. Estimating age group with one’s gait has gained wide research interest in recent times because the gait can be captured from a distance and does not any co-operation of the person for its acquisition (e.g., CCTV). Gait-based human age group estimation has numerous applications including automatic age-based access to restricted areas, monitoring in public places such as malls for age-dependent customer behavioural analysis. Male and female gaits belonging to same group widely differ and this dependence of gender on age is harnessed by devising a label encoding scheme which codes the age and the corresponding gender information as 8-bit label vector. The prospective gait-based human age estimation is done in three steps. The gait energy image (GEI) obtained from the Gait cycle forms the feature vector and the age and gender information is encoded as a label vector Next, to scale down the feature dimension, Hilbert-Schmidt Independence Criterion (HSIC) is used to learn a low dimension subspace and lastly, using k-nearest neighbour (KNN) classifier, age estimation is carried out and the age information is decoded from the label vector. The experimental results indicate that the proposed method which correlates age information with gender is effective in estimating age-group information from Gait signatures.

Multimodal biometric systems offer numerous advantages over unimodal counterparts and are being used extensively in diverse applications. However, fusion of biometric data is a non-trivial task and curtail employability of multimodal systems for a varying set of biometric characteristics with different type and dimension. Moreover, comprehensive solutions against adversary attacks that ensure template protection and prevent presentation attacks are not in place. In this article, a secure multimodal cancelable biometric system is proposed to address these concerns. This approach introduces key images based generic feature extraction technique which reduces feature dimension and achieves revocability. The non-invertibility and unlinkability are ensured through cross-diffusion of complementary information from different modalities. A new feature fusion method based on an adaptive graph is proposed to generate multimodal cancelable biometric templates. Robustness against presentation attack is accomplished through quality based adaptation of features. Extensive experimentation is performed on benchmark databases for fingerprint, face, and iris, to illustrate the efficacy of multimodal cancelable templates. The proposed approach is shown to perform favorably against state-of-the-art feature fusion methods. Furthermore, the resilience of the proposed approach against security and privacy attacks is demonstrated.

The network-based intrusion detection has become common to evaluate machine learning algorithms. Although the KDD Cup’99 Dataset has class imbalance over different intrusion classes, still it plays a significant role to evaluate machine learning algorithms. In this work, we utilize the singular valued decomposition technique for feature dimension reduction. We further reconstruct the features form reduced features and the selected eigenvectors. The reconstruction loss is used to decide the intrusion class for a given network feature. The intrusion class having the smallest reconstruction loss is accepted as the intrusion class in the network for that sample. The proposed system yield 97.90% accuracy on KDD Cup’99 dataset for the stated task. We have also analyzed the system with individual intrusion categories separately. This analysis suggests having a system with the ensemble of multiple classifiers; therefore we also created a random forest classifier. The random forest classifier performs significantly better than the SVD based system. The random forest classifier achieves 99.99% accuracy for intrusion detection on the same training and testing data set.

Since the dimension of combined feature set for partial discharge (PD) pattern recognition is higher, the corresponding sample size increases, as does the required amount of storage space and calculation, and there are features with less category-related characteristics in the feature parameters, which may contain redundant information between them. To solve the problem of higher feature dimension and complicated classification model required for the identification of partial discharge insulation defect type in this paper. Random forest sequential forward selection method based on variance analysis (RF-VA) is proposed for the optimal subset selection. This method is improved in two aspects. Firstly, a method based on variance analysis is proposed, which measures feature differences between categories, and obtains a modified arrangement displacement scheme to guide rearrangement of the order of values taken on data sample out of bag. Secondly, the sequence forward search method used to do feature selection could get iteration evaluation results, which solves randomness to determine the size of feature subset and instability of the results existing in the original algorithm. The results show RF-VA can obtain a better subset of features. It is feasible to reduce the dimension of partial discharge characteristic set, and effectively improve the identification rate of partial discharge defect type.

Achieving a reliable classification of motor imagery (MI) tasks is a major challenge in brain-computer interface (BCI) implementation. The set of relevant and discriminative features plays an important role in the classification scheme. This paper presents a supervised approach to select discriminative features for the enhancement of MI classification using multichannel electroencephalography (EEG) signal. The dimension of multiband feature space is reduced using the feature selection method. Each trial of the multichannel EEG signal representing MI tasks is decomposed into a finite set of narrowband signals. The common spatial pattern-based features are extracted from each subband. The features obtained from the multiple subbands are combined to derive a high-dimensional feature vector. The neighborhood component analysis-based feature selection method is implemented to select the features that are relevant in performing an accurate classification. It is a nearest-neighbor-based approach to learn the feature weights with regularization by maximizing the average leave-one-out classification accuracy over the labeled training data. The selected features are used to train the support vector machine for classification. The features relatively irrelevant to the classification task are discarded, yielding a reduction of feature dimension. The evaluation of the proposed method is performed using BCI Competition III dataset 4a and IV dataset 2b. Both are publicly available datasets and are used as types of benchmark data to evaluate the MI classification algorithm to implement BCI. The obtained simulation results confirm the superiority of the proposed method compared to the recently developed algorithms.

Control charts are significant diagnostic tools to detect and identify the quality fluctuation of the complex industrial process. In the practical production process, attention is being paid to the monitoring of mixture control charts, which usually coupled by two or more basic control charts modes. This research is to present a hybrid pattern recognition method for mixture control charts. The proposed method mainly covers the feature fusion extraction (FFE) and kernel extreme learning machine (KELM) with modified grey wolf optimizer (MGWO). The FFE module applies the original data and their shape and statistical features as the features, then uses kernel entropy component analysis to reduce the feature dimension and extract valid features. One significant difficulty of KELM is to get suitable parameters like the penalty parameter and the kernel function parameter value. MGWO is established to the optimal tuning of KELM parameters, which improves the population initialization and nonlinear convergence factor of traditional grey wolf optimizer. The proposed methodology is promising to obtain a better classification recognition rate, less computational time and achieves more stable results in the pattern recognition problem of mixture control charts.

With the scale expansion of industrial processes, the relationship between process variables becomes complex and highly nonlinear. As a result, the requirements for fault diagnosis and safety monitoring become demanding. To address this problem, a novel and effective pattern matching method using kernel canonical variate analysis (KCVA) integrated with an adaptive rank-order morphological filter (ARMF) is proposed for fault diagnosis. In the proposed method, KCVA is first used to extract the nonlinear correlation information with dynamic characteristics from the original process data and achieve feature dimension reduction; the features extracted by KCVA are then subjected to ARMF transformation for output trend features and pattern matching. To accurately evaluate the morphological similarity between the test trends and template trends of ARMF, the dynamic time warping distance is adopted for pattern classification. Finally, the proposed KCVA-ARMF pattern matching method is developed as an effective faul...

Local binary pattern (LBP) has already been proved to be a powerful measure of image texture with fixed sampling scheme: all P neighbor pixels in a single-scale are usually sampled by using a fixed radius R. It can effectively address grayscale and rotation variations. However, the LBP method is sensitive to image noise and fails to achieve desirable performance for texture classification with significant scale changes. With the aim to deal with these disadvantages of LBP, a new method named scale-adaptive local binary pattern (SALBP) is proposed in this paper. The essence of our proposed SALBP method is to adaptively find a single but optimal scale from multiple scales for each radial direction in accordance with the characteristics of a local area. First, we select candidates of neighbors by the majority vote strategy for signs. Second, we determine final neighbor pixels by the maximal difference magnitude selection strategy for magnitudes of candidates. This procedure lets each neighbor pixel to adaptively obtain its own optimal scale from multiple scales, which refers to the adaptive optimal sampling radius. Therefore, scale invariance can be significantly improved. Extensive experiments on four public texture databases (i.e., Outex, CUReT, UIUC and Brodatz) demonstrate that the proposed SALBP method achieves significantly better results than representative LBP variants for texture classification tasks with a smaller feature dimension.