Sparse Representation Classification Method Research Articles

The multi-channel signals based tensor sparse representation classification method for fault diagnosis of high-speed train

The transmission system is a key component to ensure the stable operation of high-speed trains. Thus, it is significant to monitor its condition to ensure the operation safety. Nowadays sparse representation is widely used in fault diagnosis. However, as the number of sensors is increasing, the existing method destroys the internal structure of multi-channel signals and cannot effectively deal with the fault diagnosis of multi-channel signals in parallel. Therefore, this article extends the existing sparse representation method to tensor space to extract the coupling information between channels and realize the fault diagnosis of multi-channel. First, a tensor sparse representation model is proposed to achieve data-level multi-channel signal fusion and complete inter-channel fault feature extraction. Then, a multimodal dictionary learning algorithm is proposed to adaptively design the data-driven dictionary to achieve data-driven feature extraction. Finally, a tensor sparse representation classification method is proposed to achieve the purpose of intelligent diagnosis. Fault experiments verify the effectiveness and superiority of the method.

A weighted sparse classification method based on period analysis dictionary

The periodic transient shocks triggered by damages in rolling bearings are frequently overshadowed by disruptive elements such as noise and harmonics. Therefore, the extraction of fault characteristics from these disturbances to identify the health status of the bearing is crucial for fault diagnosis. This study presents a novel approach, the period analysis dictionary weighted sparse representation classification (PAD-WSRC) method, designed specifically for rolling bearings. The proposed approach incorporates Bi-damped wavelet as the dictionary wavelet atom, while accounting for the pulse characteristics induced by faults and leveraging prior knowledge of periodicity. A weighted sparse representation (SR) method was also designed, which calculates the weighted SR coefficients of samples, amplifying the local features of samples while addressing the impact of time-shift bias. In addition, the bearing fault type is identified using a mutual correlation classification criterion based on sparse approximation. Our PAD-WSRC strategy has demonstrated its effectiveness in classifying the health status of bearings across three datasets, achieving recognition accuracies of 99.75%, 99.69% and 99.17%, respectively. Comparisons with several traditional methods further underscore the effectiveness and superiority of our proposed method in classifying rolling bearing faults.

A novel adaptive generalized domain data fusion-driven kernel sparse representation classification method for intelligent bearing fault diagnosis

Dictionary learning has gradually attracted attention due to its powerful feature representation ability. However, the time-shift property of collected signals hinders the recognition of various bearing states. In addition, existing dictionary learning methods are mostly designed based on a single domain, while common data fusion methods used in data-driven cannot be directly extended to dictionary learning. In this paper, a novel adaptive generalized domain data fusion-driven kernel sparse representation classification method (AGDFDK-SRC) is proposed. First, to avoid the effect of the time-shift property on dictionary learning, a class-specific kernel sub-dictionary learning method is proposed, by which the non-linear signal data is mapped into high-dimensional feature space via a kernel trick. Second, the class-specific kernel sub-dictionaries are learned by kernel K-singular value decomposition in a data-driven manner. Then, an adaptive generalized domain data fusion strategy is developed for dictionary learning, which implements data fusion of multiple domain signals to enhance the feature mining ability and representation ability of the learned dictionary. Finally, a kernel sparse classification method is designed to achieve intelligent bearing fault diagnosis. Two bearing datasets are exploited to verify the recognition performance of AGDFDK-SRC, indicating that the AGDFDK-SRC obtains superior average classification accuracies of 98.23% and 99.50%, respectively.

Effective superpixel sparse representation classification method with multiple features and L0 smoothing for hyperspectral images

Effective superpixel sparse representation classification method with multiple features and L0 smoothing for hyperspectral images

Robust Face Recognition Algorithm Based on Multidirectional Log-Gabor Features

In order to improve the robustness to variable factors such as illumination, expression and pose, a face recognition method based on multi-directional Log-Gabor feature map sparse representation is proposed. First, conduct multi-directional and multi-scale Log-Gabor transformation on the face image, and then fuse the Log-Gabor features of different scales in the same direction to obtain a multi-directional feature map. Then, extract Gist features from the fused feature map of each direction and give them adaptive weights. Then, connect the adaptive weighted Gist features of all directional feature maps to form a feature vector of the face image, Finally, sparse representation classification method is used to realize face recognition. Face recognition experiments were carried out on WIDER FACE dataset and the MAFA occlusion face dataset, and the identification accuracy of the proposed method is compared with SRC, NMR, Gabor NLDA NN and Gabor NLDA SVM. The experimental results show that the method in this paper can effectively describe the local face information by using multi-directional Log Gabor feature map (MGFM) to establish the feature representation of the face image, and the improved cooperative representation classification algorithm using adaptive K nearest neighbor strategy can achieve higher recognition accuracy.

Industrial load identification based on sparse representation classification

In this paper, sparse representation classification (SRC) model based on discriminant dictionary is proposed to train loads of different categories by K-SVD algorithm. Firstly, The absolute value of sparse coefficient and the category of atoms are used to determine the load category. Secondly, the identification of load types is improved in the traditional SRC method based on dictionary learning. Thirdly, the actual identify effect is optimized. Finally, an example is given to illustrate the recognition process. The discriminant dictionary-based SRC method is compared with the traditional SRC method. It proved the feasibility and effectiveness of the method.

Application of improved virtual sample and sparse representation in face recognition

AbstractSparse representation plays an important role in the research of face recognition. As a deformable sample classification task, face recognition is often used to test the performance of classification algorithms. In face recognition, differences in expression, angle, posture, and lighting conditions have become key factors that affect recognition accuracy. Essentially, there may be significant differences between different image samples of the same face, which makes image classification very difficult. Therefore, how to build a robust virtual image representation becomes a vital issue. To solve the above problems, this paper proposes a novel image classification algorithm. First, to better retain the global features and contour information of the original sample, the algorithm uses an improved non‐linear image representation method to highlight the low‐intensity and high‐intensity pixels of the original training sample, thus generating a virtual sample. Second, by the principle of sparse representation, the linear expression coefficients of the original sample and the virtual sample can be calculated, respectively. After obtaining these two types of coefficients, calculate the distances between the original sample and the test sample and the distance between the virtual sample and the test sample. These two distances are converted into distance scores. Finally, a simple and effective weight fusion scheme is adopted to fuse the classification scores of the original image and the virtual image. The fused score will determine the final classification result. The experimental results show that the proposed method outperforms other typical sparse representation classification methods.

Enhanced dictionary learning based sparse classification approach with applications to planetary bearing fault diagnosis

To date, planetary bearings remain challenging for machinery fault diagnosis because of their intricate kinematics, time-variant modulations, and strong interferences. To address this challenge, this study presents an enhanced dictionary learning based sparse classification (EDL-SC) approach to diagnose planetary bearings. Our main novelty lies in that the data augmentation and dictionary learning strategies are incorporated into our proposed EDL-SC approach, which can significantly enhance the representation ability and recognition ability for the sparse classification-based intelligent diagnosis criterion. Firstly, vibration data augmentation is implemented with an overlapping segmentation strategy to enhance the quality of training samples. Secondly, data-driven dictionary design is achieved by means of dictionary learning, which learns sub-dictionaries and adaptively designs the whole dictionary through considering both the inter-class and intra-class features. Thirdly, a sparse classification strategy is established for intelligent diagnostics by the aid of a discrimination criterion of minimal reconstruction errors. The feasibility and advantage of EDL-SC have been thoroughly evaluated with a challenging planetary bearing dataset. Experiment verification results of planetary bearing fault diagnosis indicate that EDL-SC obtains a superior diagnosis accuracy of 99.63%, strong robustness to noises, and competitive computation efficiency over advanced deep learning and sparse representation classification methods. This work can bring new insights for the application of sparse representation theory from the perspective of pattern recognition, and shows great potentials of EDL-SC for data-driven machinery fault diagnosis.

Change detection based on unsupervised sparse representation for fundus image pair

Detecting changes is an important issue for ophthalmology to compare longitudinal fundus images at different stages and obtain change regions. Illumination variations bring distractions on the change regions by the pixel-by-pixel comparison. In this paper, a new unsupervised change detection method based on sparse representation classification (SRC) is proposed for the fundus image pair. First, the local neighborhood patches are extracted from the reference image to build a dictionary of the local background. Then the current image patch is represented sparsely and its background is reconstructed by the obtained dictionary. Finally, change regions are given through background subtracting. The SRC method can correct automatically illumination variations through the representation coefficients and filter local contrast and global intensity effectively. In experiments of this paper, the AUC and mAP values of SRC method are 0.9858 and 0.8647 respectively for the image pair with small lesions; the AUC and mAP values of the fusion method of IRHSF and SRC are 0.9892 and 0.9692 separately for the image pair with the big change region. Experiments show that the proposed method in this paper is more robust than RPCA for the illumination variations and can detect change regions more effectively than pixel-wised image differencing.

Corresponding keypoint constrained sparse representation three‐dimensional ear recognition via one sample per person

When only one sample per person (OSPP) is registered in the gallery, it is difficult for ear recognition methods to sufficiently and effectively reduce the search range of the matching features, thus resulting in low computational efficiency and mismatch problems. A 3D ear biometric system using OSPP is proposed to solve this problem. By categorising ear images by shape and establishing the corresponding relationship between keypoints from ear images and regions (regional cluster) on the directional proposals that can be arranged to roughly face the ear image, the corresponding keypoints are obtained. Then, ear recognition is performed by combining corresponding keypoints and a multi-keypoint descriptor sparse representation classification method. The experimental results conducted on the University of Notre Dame Collection J2 dataset yielded a rank-1 recognition rate of 98.84%; furthermore, the time for one identification operation shared by each gallery subject was 0.047 ms.

Vehicle Image Recognition Using Deep Convolution Neural Network and Compressed Dictionary Learning

In this paper, a vehicle recognition algorithm based on deep convolutional neural network and compression dictionary is proposed. Firstly, the network structure of fine vehicle recognition based on convolutional neural network is introduced. Then, a vehicle recognition system based on multi-scale pyramid convolutional neural network is constructed. The contribution of different networks to the recognition results is adjusted by the adaptive fusion method that adjusts the network according to the recognition accuracy of a single network. The proportion of output in the network output of the entire multiscale network. Then, the compressed dictionary learning and the data dimension reduction are carried out using the effective block structure method combined with very sparse random projection matrix, which solves the computational complexity caused by high-dimensional features and shortens the dictionary learning time. Finally, the sparse representation classification method is used to realize vehicle type recognition. The experimental results show that the detection effect of the proposed algorithm is stable in sunny, cloudy and rainy weather, and it has strong adaptability to typical application scenarios such as occlusion and blurring, with an average recognition rate of more than 95%.

Sparse Representation Classification Method for Active Sonar Target Based on Multi-order Fractional Fourier Domain Feature Fusion

Marine environment noise and reverberation interference are serious, and the poor target separability is the bottleneck problem in active sonar target classification and recognition. In order to solve this problem, based on the echo signal model of active sonar target and the principle of FRactional Fourier Transform (FRFT), this paper deduces the multi-order FRFT domain feature representation form, establishes the FRFT domain sparse representation model, and proposes a method to classify the sparse representation of active sonar targets with multi-order FRFT domain feature fusion. The method achieves the purpose of suppressing noise and reverberation interference through the energy aggregation of FRFT and removing the residual of sparse decomposition; Through the fusion of multi-order FRFT domain features, the separability of targets is improved, and the active sonar target classification with low SNR is realized. Experimental results show that the classification accuracy of the proposed method can reach more than 90% when the SNR is about 0 dB.

An Intelligent EEG Classification Methodology Based on Sparse Representation Enhanced Deep Learning Networks.

The classification of electroencephalogram (EEG) signals is of significant importance in brain–computer interface (BCI) systems. Aiming to achieve intelligent classification of EEG types with high accuracy, a classification methodology using sparse representation (SR) and fast compression residual convolutional neural networks (FCRes-CNNs) is proposed. In the proposed methodology, EEG waveforms of classes 1 and 2 are segmented into subsignals, and 140 experimental samples were achieved for each type of EEG signal. The common spatial patterns algorithm is used to obtain the features of the EEG signal. Subsequently, the redundant dictionary with sparse representation is constructed based on these features. Finally, the samples of the EEG types were imported into the FCRes-CNN model having fast down-sampling module and residual block structural units to be identified and classified. The datasets from BCI Competition 2005 (dataset IVa) and BCI Competition 2003 (dataset III) were used to test the performance of the proposed deep learning classifier. The classification experiments show that the recognition averaged accuracy of the proposed method is 98.82%. The experimental results show that the classification method provides better classification performance compared with sparse representation classification (SRC) method. The method can be applied successfully to BCI systems where the amount of data is large due to daily recording.

The Contrast-Enhanced Ultrasound Information Technology in the Treatment of Liver Tumors

Objective: To study the automatic diagnosis method of liver tumors in the contrast-enhanced ultrasound environment, assist doctors in the clinical diagnosis of liver tumors intuitively, conveniently, and accurately, thereby improve the cure rate of liver tumors. Methods: First, six sets of experimental data were constructed. The automatic diagnosis experiment of liver tumors through contrast-enhanced ultrasound was performed by the combination of sparse representation-based support vector machine (SVM) and principal component analysis (PCA)-based SVM, as well as the sparse representation classification method. The effect of classification decision principles on experiments was further studied. Results: The SVM method had an average effect on diagnostic accuracy. The average diagnostic accuracy of the six different experimental data sets was 76%, and the average diagnosis time was 300 s. The feature extraction method based on the combination of sparse representation and PCA was applied to the SVM method to achieve an optimal diagnosis. The average diagnosis accuracy rate could reach 87%, and the average time was more than 1,000 s. Using the sparse classification representation method, the diagnostic accuracy rate for the six experimental data sets constructed was above 93%, with a maximum of 99%, and the average time was 210 s. The sparse classification representation using the principle of minimum reconstruction error classification decision had an average diagnostic accuracy rate of 99% and an average time of 128 s. Conclusion: The sparse classification representation for the clinical diagnosis of liver tumors by contrast-enhanced ultrasound had high accuracy and consumed less time. Therefore, the constructed method was valuable.

Superpixel-feature-based multiple kernel sparse representation for hyperspectral image classification

Spatial information explored by superpixels can provide significant improvement for the accuracy of hyperspectral image classification. However, superpixels based sparse representation classification (SSRC) methods always introduce joint sparse pattern, which leads to the higher computational complexity. In this paper, a novel superpixel-feature-based multiple kernel sparse representation classification (SFMKSRC) method is proposed. Different from the traditional SSRC methods, SFMKSRC develops two new kinds of superpixel-based features by exploiting local mean operator within superpixels and weighted average operator among superpixels, which can describe the spatial information from both the local and global perspectives. Also, the novel superpixel-based features can avoid joint reconstruction to reduce the computational complexity. Then, the spectral-spatial features are extracted to combine with superpixels-based features and capture the detail structures of HSI to improve the classification performance. As pixels in high-dimensional feature space always trend to be linearly inseparable, a novel SFMKSRC model is designed to address the linearly inseparable problem of HSI classification. In addition, composite kernel constructed by generating base kernels for different features and optimally determining the weights of base kernels is embedded into MKSRC to exploit the strong correlations among different features while still preserve their diversities in a more flexible way. Experimental results on three real-world HSI datasets demonstrated that the proposed SFMKSRC method obtains a competitive performance and outperforms several state-of-the-art classification methods.

Image classification algorithm based on stacked sparse coding deep learning model-optimized kernel function nonnegative sparse representation

Image classification has received extensive attention as an important technical means of acquiring image information. It has been widely used in various engineering fields. Although the existing traditional image classification methods have been widely applied in practical problems, there are some problems in the application process, such as unsatisfactory effects, low classification accuracy and weak adaptive ability. This is because this type of method relies on the designer’s prior knowledge and cognitive understanding of the classification task. At the same time, this method separates image feature extraction and classification into two steps for classification operation. However, the deep learning model has a powerful learning ability, which integrates the feature extraction and classification process into a whole to complete the image classification test, which can effectively improve the image classification accuracy. At the same time, the image classification method based on deep learning also has the following problems in the application process: First, it is impossible to effectively approximate the complex functions in the deep learning model. Second, the deep learning model comes with a low classifier with low accuracy. To this end, this paper introduces the idea of sparse representation into the architecture of deep learning network, comprehensively utilizes the sparse representation of good multidimensional data linear decomposition ability and the deep structural advantages of multi-layer nonlinear mapping to complete the complex function approximation in deep learning model. It constructs a deep learning model with adaptive approximation ability, which solves the function approximation problem of deep learning models. At the same time, in order to further improve the classification effect of the deep learning classifier, a sparse representation classification method based on the optimized kernel function is proposed to replace the classifier in the deep learning model, thereby improving the image classification effect. Based on the above explanation, this paper proposes an image classification algorithm based on the stacked sparse coding depth learning model-optimized kernel function nonnegative sparse representation. The experimental results show that the proposed method not only has a higher average accuracy than other mainstream methods, but also can be well adapted to various image databases. This is because the proposed method can extract more image feature information than the traditional image classification method and can better adaptively match the image information. Compared with other deep learning methods, it can better solve the problems of complex function approximation and poor classifier effect, thus further improving image classification accuracy.

Image Classification Algorithm Based on Deep Learning-Kernel Function

Although the existing traditional image classification methods have been widely applied in practical problems, there are some problems in the application process, such as unsatisfactory effects, low classification accuracy, and weak adaptive ability. This method separates image feature extraction and classification into two steps for classification operation. The deep learning model has a powerful learning ability, which integrates the feature extraction and classification process into a whole to complete the image classification test, which can effectively improve the image classification accuracy. However, this method has the following problems in the application process: first, it is impossible to effectively approximate the complex functions in the deep learning model. Second, the deep learning model comes with a low classifier with low accuracy. So, this paper introduces the idea of sparse representation into the architecture of the deep learning network and comprehensively utilizes the sparse representation of well multidimensional data linear decomposition ability and the deep structural advantages of multilayer nonlinear mapping to complete the complex function approximation in the deep learning model. And a sparse representation classification method based on the optimized kernel function is proposed to replace the classifier in the deep learning model, thereby improving the image classification effect. Therefore, this paper proposes an image classification algorithm based on the stacked sparse coding depth learning model-optimized kernel function nonnegative sparse representation. The experimental results show that the proposed method not only has a higher average accuracy than other mainstream methods but also can be good adapted to various image databases. Compared with other deep learning methods, it can better solve the problems of complex function approximation and poor classifier effect, thus further improving image classification accuracy.

Discriminative Face Recognition Methods with Structure and Label Information via $$l_2$$-Norm Regularization

Existing sparse representation methods either fail to incorporate the structure and label information of training samples, or suffer from expensive computation for $$l_1$$- or $$l_{2,1}$$-norm. In this paper, we propose three discriminative sparse representation classification methods with structure and label information based on $$l_2$$-norm regularization for robust face recognition. We propose the first classification method with structure and label information by enforcing competition among the representation results of training samples from different classes in representing a test sample. To make the classification more discriminative, we present the decorrelation classification method with structure and label information by jointly considering the competition and decorrelation regularizations. In addition, by incorporating the locality information of samples, we propose the third method called locality-constrained decorrelation classification method with structure and label information. The proposed methods not only contain the structure and label information of training samples, but also have low computational cost owing to the use of $$l_2$$-norm. All three methods have closed-form solutions, rendering them easy to solve and calculate efficiently. Importantly, the proposed methods can achieve better recognition results than most existing state-of-the-art sparse representation methods. Furthermore, based on the proposed methods, we illustrate the effect of different regularization constraints on the recognition performance. Experiments on the ORL, Extended YaleB, FERET, and LFW databases validate the effectiveness of the proposed methods.

From Real to Complex

Activity recognition is an important component of many pervasive computing applications. Radio-based activity recognition has the advantage that it does not have the privacy concern compared with camera-based solutions, and subjects do not have to carry a device on them. It has been shown channel state information (CSI) can be used for activity recognition in a device-free setting. With the proliferation of wireless devices, it is important to understand how radio frequency interference (RFI) can impact on pervasive computing applications. In this article, we investigate the impact of RFI on device-free CSI-based location-oriented activity recognition. We present data to show that RFI can have a significant impact on the CSI vectors. In the absence of RFI, different activities give rise to different CSI vectors that can be differentiated visually. However, in the presence of RFI, the CSI vectors become much noisier, and activity recognition also becomes harder. Our extensive experiments show that the performance may degrade significantly with RFI. We then propose a number of countermeasures to mitigate the impact of RFI and improve the performance. We are also the first to use complex-valued CSI along with the state-of-the-art Sparse Representation Classification method to enhance the performance in the environment with RFI.

Region‐division‐based joint sparse representation classification for hyperspectral images

In this article, a region-division-based joint sparse representation classification (RDJSRC) method is proposed to solve the heterogeneous region problem in the joint sparse representation classification (JSRC) method used in hyperspectral image (HSI) classification. The RDJSRC method incorporates regional information, obtained by the hidden Markov random field (HMRF), into the JSRC to reduce the interference of heterogeneous pixels in the neighbourhood of the test pixel and finally improve the classification performance. The framework of this method is as follows. The first several principal components (PCs) are initially selected to be the new HSI by transforming the original HSI with the PC analysis algorithm. Then, the regional information containing the spatial structure of the HSI is obtained by applying the HMRF algorithm to the first PC. Through incorporating this regional information into the JSRC procedure, the initial label of the test pixel can be jointly determined by the new HSI pixels within the homogeneity in the search window. Ultimately, the final label of the test pixel is determined by a voting strategy based on multiple classification results. Compared with several classification methods, experimental results, indicate that this method achieves improvement from 2 to 3% in HSI classification.