A Novel Weighted Sparse Representation Classification Strategy Based on Dictionary Learning for Rotating Machinery

Marine controlled source electromagnetic (CSEM) is an efficient method to explore ocean resources. The amplitudes of marine CSEM signals decay rapidly with the measuring offsets. The signal is easily contaminated by various kinds of noise when the offset is large. These noise include instrument internal noise, dipole vibration noise, seawater motion noise and environmental noise Suppressing noise is the key to improve data quality and interpretation accuracy. Sparse representation based denoising method has been used for denoising for a long time. provides a new way to remove noise. Under the framework of sparse representation, the denoising effect is closely related to the chosen transform matrix. This matrix is called dictionary and its column named atom. In general, the stronger the correlation between signal and dictionary is, the sparser representation will be, and further the better the denoising effect will be. In this article, a new method based on dictionary learning is proposed for marine CSEM denoising. Firstly, the signal segments suffering little from noise are captured to compose the training set. Then the learned dictionary is trained from the training set via K-singular value decomposition (K-SVD) algorithm. Finally, the learned dictionary is used to sparsely represent the contaminated signal and reconstruct the filtered one. The effectiveness of the proposed approach is verified by a synthetic data denoising experiment, in which windowed-Fourier-transform (WFT) and wavelet-transform (WT) denoising methods and three dictionaries (discrete-sine-transform (DST) dictionary, DST-wavelet merged dictionary and the learned dictionary) under a sparse representation framework are tested. The results demonstrate the superiority of the proposed dictionary-learning-based denoising method. Finally, the proposed approach is applied to field data denoising process, coupled with DST and DST-wavelet dictionaries based denoising methods. The outcomes further proves that the propsoed approach is effective and superior for marine CSEM data denoising.

Based on multiple maximum scatter difference discrimination Dictionary learning, a novel face recognition algorithm is proposed. Dictionary used for sparse coding plays a key role in sparse representation classification. In this paper, a multiple maximum scatter difference discriminated criterion is used for dictionary learning. During the process of dictionary learning, the multiple maximum scatter difference computes its discriminated vectors from both the range of the between class scatter matrix and the null space of the within-class scatter matrix. The proposed algorithm is theoretically elegant and easy to calculate. Extensive experimental studies conducted on the AR database and Extended Yale Database B in comparison with existing basic sparse representation and other classification methods, it shows that the performance is a little better than the original sparse representation methods with lower complexity.

Owing to complicated manual design and feature extraction processes of mechanical multi-fault classification and recognition methods, single-class classifiers do not adapt well to the high dimensionality and high self-similarity among mechanical vibration signals with nonlinear characteristics, resulting in insufficient classification accuracy. Combining the adaptive learning and feature extraction capabilities of the wavelet kernel-based extreme learning machine (WKELM) with the sparse representation classifier (SRC), this paper proposes a hybrid artificial bee colony (ABC)-optimized Morlet wavelet kernel-based extreme learning machine (ABC-MWKELM) with SRC. The two-level classifier is capable of cooperatively classifying multiple mechanical faults. First, a Morlet wavelet kernel-based extreme learning machine (MWKELM) model that matches the kernel function construction condition is presented. An ABC optimization algorithm is adopted to optimize critical parameters of the MWKELM model, thereby improving the accuracy and generalization ability of the model. In addition, the ABC algorithm is used to optimize the MWKELM model as a pre-classifier for classifying test samples. According to the posterior probability of each training sample category output, the threshold decision method selects candidate categories with a high confidence coefficient. When the preset meets the threshold, the classification result of the ABC-optimized MWKELM is directly adopted. Otherwise, the candidate training samples are used to construct a dictionary and the SRC is used to classify the test samples further. The experimental results demonstrate that compared with the ELM, WKELM, (ELM-SRC), and radial basis function (RBF)-KELM and SRC (RBFKELM-SRC), the method presented in this paper has significant advantages in terms of accuracy of mechanical multi-fault classification. The validity and applicability of this method in mechanical multi-fault classification were verified.

A new method classifying power quality disturbances (PQD) based on random matrix transform (RMT) and sparse representation classification (SRC) by L1-minimization is presented. First, the PQD signals are characterized by random matrix lower-dimensional projection based on compressive sensing theory. Then, every test sample from feature vectors is represented as a sparse linear combination of training samples. The PQD type assign to the object class that minimizes the residual between test sample and its sparse representation by solving L1-minimization problem. RMT feature extraction method is extremely efficient to generate and independent of the training dataset. Compared with support vector machine (SVM), the SRC algorithm needs neither training process nor combination of two-class classifiers for multiclass classification. Simulation results show that the proposed feature extraction and classification method has high classification correct ratio in strong noise condition.

In this paper, an efficient finger vein recognition algorithm based on the combination of the nearest centroid neighbor and sparse representation classification techniques ( ${k}$ NCN-SRC) is presented. The previously proposed recognition algorithms are mainly based on distance computation. In the proposed method, the distance, as well as the spatial distribution, are considered to achieve a better recognition rate. The proposed method consists of two stages: first, the ${k}$ nearest neighbors of the test sample are selected based on the nearest centroid neighbor, and then, in the second stage, based on the selected number of closest nearest centroid neighbors ( ${k}$ ), the test sample is classified by sparse representation. Findings from the proposed method ${k}$ NCN-SRC demonstrated an increased recognition rate. This improvement can be attributed to the selection of the train samples, where the train samples are selected by considering the spatial and distance distribution. In addition, the complexity of SRC is reduced by reducing the number of train samples for the classification of the test sample by sparse representation and the processing speed of the proposed algorithm is significantly improved in comparison with the conventional SRC which is due to the reduced number of training samples. It can be concluded that the ${k}$ NCN-SRC classification method is efficient for finger vein recognition. An increase in the recognition rate of 3.35% , 9.07% , 20.23% , and 0.81% is obtained for the proposed ${k}$ NCN-SRC method in comparison with the conventional SRC for the four tested public finger vein databases.

Semi-supervised two phase test sample sparse representation classifier

Multivariate classification techniques have been widely applied to decode brain states using functional magnetic resonance imaging (fMRI). Due to variabilities in fMRI data and the limitation of the collection of human fMRI data, it is not easy to train an efficient and robust supervised-learning classifier for fMRI data. Among various classification techniques, sparse representation classifier (SRC) exhibits a state-of-the-art classification performance in image classification. However, SRC has rarely been applied to fMRI-based decoding. This study aimed to improve SRC using unlabeled testing samples to allow it to be effectively applied to fMRI-based decoding. We proposed a semisupervised-learning SRC with an average coefficient (semiSRC-AVE) method that performed the classification using the average coefficient of each class instead of the reconstruction error and selectively updated the training dataset using new testing data with high confidence to improve the performance of SRC. Simulated and real fMRI experiments were performed to investigate the feasibility and robustness of semiSRC-AVE. The results of the simulated and real fMRI experiments showed that semiSRC-AVE significantly outperformed supervised learning SRC with an average coefficient (SRC-AVE) method and showed better performance than the other three semisupervised learning methods.

This paper proposes a novel vehicle-type classifier named SRCVT that uses video data collected from video detection units. The SRCVT uses the sparse representation classifier (SRC) technique without the requirement of an additional training procedure to construct the classification model. It classifies preceding vehicles directly from the testing samples’ sparse representation, without the need for explicit model selection. The SRCVT consists of four steps: data preparation, principal component analysis transformation, realization, and classification output. The classifier has been compared with the traditional method of using a supported vector machine. The results show that the SRCVT is more promising for vehicle-type classification in terms of classification accuracy and ease of use.

Sparse representation classification (SRC) has been successfully applied into hyperspectral image (HSI). A test sample (pixel) can be linearly represented by a few training samples of the training set. The class label of the test sample is then decided by the reconstruction residuals. To incorporate the spatial information to improve the classification performance, a patch matrix, which includes a spatial neighborhood set, is used to replace the original pixel. Generally, the objective function of the reconstruction residuals is represented as Frobenius-norm, which actually treats the elements in the reconstruction residuals in the same way. However, when a patch locates in the image edge, the samples in the patch may belong to different classes. Frobenius-norm is not suitable to compute the reconstruction residuals. In this paper, we propose a robust patch-based sparse representation classification (RPSRC) based on [Formula: see text]-norm. An iteration algorithm is given to compute RPSRC efficiently. Extensive experimental results on two real-life HSI datasets demonstrate the effectiveness of RPSRC.

Facial expressions play important role in human communication. The understanding of facial expression is a basic requirement in the development of next generation human computer interaction systems. Researches show that the intrinsic facial features always hide in low dimensional facial subspaces. This paper presents facial parts based facial expression recognition system with sparse representation classifier. Sparse representation classifier exploits sparse representation to select face features and classify facial expressions. The sparse solution is obtained by solving l¹ -norm minimization problem with constraint of linear combination equation. Experimental results show that sparse representation is efficient for facial expression recognition and sparse representation classifier obtain much higher recognition accuracies than other compared methods.

Non-overlapping classification of hyperspectral imagery with superpixel segmentation

K-Singular Value Decomposition (K-SVD) is an excellent feature analysis method, but it has a large amount of calculation and low algorithm efficiency. To solve the above problems, a dictionary learning method for image block classification training is proposed. This method uses image entropy as the classification index of image samples, selects more complex texture image blocks with more analytical value, and uses Discrete Cosine Transform- Discrete Wavelet Transformation (DCT-DWT) dictionary to train them in K-Singular Value Decomposition. Through the feedback of sparse coefficients, the threshold value is dynamically changed and the precision of sparse coding is controlled. In the sparse solution, a variable-step Orthogonal Matching Pursuit (OMP) algorithm is used. The experimental comparison shows that the classification K-Singular Value Decomposition dictionary learning algorithm runs faster, and the Peak signal-to-noise Ratio (PSNR) and Structural Similarity (SSIM) value of the reconstructed image are higher.

Compressed sensing (CS) has gained much interests in the speech processing community and especially in compression. The success of this appealing paradigm relies, heavily, on the sparsity of speech signals in a given dictionary. Thanks to machine learning, it is possible to go beyond the limits of analytical dictionaries by designing learned dictionaries, which are more able to fit the nature of data. In this paper, we propose speech compression scheme based on CS using K-singular value decomposition (K-SVD) algorithm to learn a dictionary for sparse representation and iteratively reweighted least square (IRLS) algorithm for signal reconstruction from randomly generated measurements. Different sizes of dictionaries are trained and compared with discrete cosine transform (DCT). Signal quality results, measured with perceptual evaluation speech quality (PESQ) revealed that learned dictionary using K-SVD improves the performance of speech compressed sensing comparing to DCT.

Aiming at the problems of complex data processing and difficult identification caused by noise interference and other factors in the process of fault type identification of distribution network, this paper proposes a fault type identification method of distribution network based on sparse representation. The collection of fault data does not rely on the Shannon Theory. The collected time domain three-phase voltage(TPV) and zero-sequence voltage(ZSV) are divided into training sets and test sets. Learn the characteristic information of the fault voltage of the training set by the K-SVD dictionary learning algorithm, and construct an over-complete dictionary that accurately matches the essential characteristics of various types of faults. Based on learning dictionaries, the orthogonal matching pursuit (OMP) algorithm is used to perform adaptive dictionary sparse decomposition of the original time-domain fault data of the test set, and the fault signal is decomposed into the product of the overcomplete dictionary and the sparse vector. Combined with the classification method based on sparse Representation(SRC), the classification of the reconstructed fault signal is achieved. The proposed fault classification method does not need to construct fault signal features manually and avoids the tedious process of fault signal feature screening. It has strong adaptability and is not susceptible to transition resistance and noise. MATLAB/Simulink simulation verifies the effectiveness and accuracy of the proposed method.

Sparse representation in compressed sensing is a hot topic in signal processing and artificial intelligence due to its success in various applications. A general classification algorithm based on sparse representation theory named Sparse Representation Classification (SRC) was successfully applied in face recognition. In this paper, we research the issue of facial expression recognition (FER) via SRC. Extensive experiments of FER via SRC algorithm are carried out to study the performance of sparse representation theory for FER. The comparison of SRC algorithm with various traditional algorithms such as two-dimensional PCA as well as curvelet transform for FER is also given. Support vector machine is used for expression classification. The paper also studies the robustness of SRC algorithm for FER to noises. Satisfactory results are obtained for FER via sparse representation. The experiment results show the effectiveness of SRC algorithm on FER.