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.

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.

Spectrum analysis can quickly extract and analyze frequency domain features of signal, and it has been widely applied in fields of image processing, noise processing and signal processing. Fast Fourier transform (FFT) is fast and efficient, because it can efficiently decrease complexity of discrete Fourier transform. As a consequence, FFT is a very good method for image frequency spectrum analysis. In this paper, we propose to consolidate frequency domain representation by sparse representation (SR) and collaborative representation classification (CRC) which has excellent performance in comparison with general sparse representation-associated classification algorithms. Our proposed novel method has three main phases. The first phase utilizes FFT to extract frequency domain features of original images, which are complementary with representations of the original images. The second phase of our proposed novel method exploits CRC or SR to obtain scores of original images and obtained features, respectively. The third phase integrates the scores of original images and obtained features and uses them to classify images. The major contribution of the proposed method is that it is usually more robust than methods using only FFT, CRC or state-of-art method CIRLRC for image classification. The experiments of image classification demonstrate that the simultaneous use of FFT and CRC or sparse representation classification has high accuracy on image recognition.

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.

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.

The development of accurate and robust palmprint recognition algorithm is a critical issue in automatic palmprint recognition system. In this paper, we propose a palmprint recognition method based on a two-phase test sample sparse representation. In the first phase, a test sample is represented as a linear combination of all the training samples and m "nearest neighbors" are selected based on the representation ability. In the second phase, the test sample is represented as a linear combination of the determined m nearest neighbors and the representation result is used for classification. Experimental results on PolyU database show the effectiveness of the proposed method in terms of recognition rate.

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.

Recognition of emotional content in music is an issue that arises recently. Music received by live applications are often exposed to noise, thus prone to reducing the recognition rate of the application. The solution proposed in this study is a robust music emotion recognition system for live applications. The proposed system consists of two major parts, i.e. subspace-based noise suppression and a hierarchical sparse representation classifier, which is based on sparse coding and a sparse representation classifier (SRC). The music is firstly enhanced by fast subspace based noise suppression. Nine classes of emotion are then used to construct a dictionary, and the vector of coefficients is obtained by sparse coding. The vector can be divided into nine parts, and each of which models a specific emotional class of a signal. Since the proposed descriptor can provide emotional content analysis of different resolutions for emotional music recognition, this work regards vectors of coefficients as feature representations. Finally, a sparse representation based classification method is employed for classification of music into four emotional classes. The experimental results confirm the highly robust performance of the proposed system in emotion recognition in live music.

Sparse Representation Classifier (SRC) and its variants were considered as powerful classifiers in the domains of computer vision and pattern recognition. However, classifying test samples is computationally expensive due to the $$\ell _1$$ norm minimization problem that should be solved in order to get the sparse code. Therefore, these classifiers could not be the right choice for scenarios requiring fast classification. In order to overcome the expensive computational cost of SRC, a two-phase coding classifier based on classic Regularized Least Square was proposed. This classifier is more efficient than SRC. A significant limitation of this classifier is the fact that the number of the samples that should be handed over to the next coding phase should be specified a priori. This paper overcomes this main limitation and proposes five data-driven schemes allowing an automatic estimation of the optimal size of the local samples. These schemes handle the three cases that are encountered in any learning system: supervised, unsupervised, and semi-supervised. Experiments are conducted on five image datasets. These experiments show that the introduced learning schemes can improve the performance of the two-phase linear coding classifier adopting ad-hoc choices for the number of local samples.

Rotating machinery is widely applied in industrial fields. However, it generally operates under tough working conditions, which leads to the weak fault features and renders fault diagnosis more difficult. In this case, an emerging method called sparse representation classification (SRC) is proposed to enhance the fault features and identify the fault status. However, the typical SRC theory fails to consider the locality of the test sample and training sample, and the training set generally contains much redundant information, which may reduce the fault recognition accuracy. Moreover, the time-shift deviation of vibration signal cannot be avoided effectively using a typical SRC model. To overcome the above-mentioned problems, a novel SRC model, i.e., weighted SRC based on dictionary learning (DL-WSRC), is proposed. For the training set, different fault signals are learned based on the improved K-singular value decomposition (K-SVD) algorithm, which can not only adaptively update the whole training set but also reduce redundant information so as to enhance the sample fault features. For the test sample, DL-WSRC selects an accurate time-domain parameter using the K-means clustering algorithm and computes the weighted coefficients according to the parameter distance between the test sample and the training samples. Then, it sparsely represents the test sample by solving a weighted $l_{0}$ -norm problem. The goal of weighting is to pay more attention to the locality of the sample so as to improve the recognition accuracy. Finally, according to the results of sparse representation, the fault status can be identified through the correlation analysis, which can effectively solve the time-shift deviation problem. The effectiveness of the proposed method is validated by the experiments of rotating machinery, and the results indicate that the proposed method realizes fault classification with a high accuracy.

With the rapid development of information science and technology, image recognition technology plays an increasingly important role in the field of information security. However, in practical application, image recognition is easily affected by factors such as illumination, occlusion, background and other non-ideal conditions, so it is of great practical significance to seek robust image recognition technology. Sparse representation and collaborative representation can capture the essential features of face image, and obtain better recognition effect in image recognition. Therefore, this paper proposes an image recognition algorithm based on information fusion of sparsity and synergy. Experiments are carried out on the problems of collaborative representation classification and single sample image recognition. Experimental results show that, compared with sparse representation classification, collaborative representation classification achieves higher classification accuracy. When part of the pixel value image is occluded by 10%, the recognition rate of sparse representation algorithm is 99.1%, and the recognition rate is very good. Both algorithms have achieved very good recognition results in image recognition. Experiments show that sparse representation algorithm and collaborative representation algorithm improve the recognition rate of images.

The Sparse Representation for Classification (SRC) algorithm has been demonstrated to be a state-of-the-art algorithm for facial recognition applications. Wright et al. demonstrate that under certain conditions, the SRC algorithm classification performance is agnostic to choice of linear feature space and highly resilient to image corruption. In this work, we examined the SRC algorithm performance on the vehicle recognition application, using images from the semi-synthetic vehicle database generated by the Air Force Research Laboratory. To represent modern operating conditions, vehicle images were corrupted with noise, blurring, and occlusion, with representation of varying pose and lighting conditions. Experiments suggest that linear feature space selection is important, particularly in the cases involving corrupted images. Overall, the SRC algorithm consistently outperforms a standard k nearest neighbor classifier on the vehicle recognition task.

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.

Graph construction using adaptive Local Hybrid Coding scheme

The sparse representation classifier (SRC) has been developed to offer a formulation to the face recognition problem under scene dependent conditions, such as illumination/pose variations, occlusion, and disguise. However, this method has not considered image quality degradations resulting from capture, such as blur, under the same scene variations. In this work, we explore the performance of the well-known face recognition framework SRC in the presence of Gaussian blur in both constrained and unconstrained environments. Finally, we propose an augmented SRC (ASRC) framework to improve the performance of the original SRC in the presence of Gaussian blur, while preserving its robustness to scene dependent variations.