Kernel Sparse Representation-based Classification Research Articles

Multi-View Kernel Sparse Representation for Identification of Membrane Protein Types.

Membrane proteins are the main undertaker of biomembrane functions and play a vital role in many biological activities of organisms. Prediction of membrane protein types has a great help in determining the function of proteins and understanding the interactions of membrane proteins. However, the biochemical experiment is expensive and not suitable for the large-scale identification of membrane protein types. Therefore, computational methods were used to improve the efficiency of biological experiments. Most existing computational methods only use a single feature of protein, or use multiple features but do not integrate these well. In our study, the protein sequence is described via three different views (features), including amino acid composition, evolutionary information and physicochemical properties of amino acids. To exploit information among all views (features), we introduce a coupling strategy for Kernel Sparse Representation based Classification (KSRC) and construct a new model called Multi-view KSRC (MvKSRC). We implement our method on 4 benchmark data sets of membrane proteins. The comparison results indicate that our method is much superior to all existing methods.

A new greedy sparse recovery algorithm for fast solving sparse representation

Kernel sparse representation-based classification (KSRC) in compressive sensing represents one of the most interesting research areas in pattern recognition and image processing. Nevertheless, KSRC is subjected to some shortcomings. KSRC is greedy in time to achieve an approximate solution of sparse representation based on $$\ell_{1}$$ -norm minimization. A diversity of greedy recovery algorithms have been tested in order to decrease computational complexity compared to the optimal $$\ell_{1}$$ -norm minimization while keeping a proved reconstruction accuracy. In this research, we suggest a new greedy recovery algorithm, called the fast reduced sampling matching pursuit (FRSMP). Unlike previous greedy recovery algorithms which applied too many or very few values per iteration, FRSMP selects a sufficient number of elements. Moreover, FRSMP performs the least square minimization iteratively through the Sherman–Morrison–Woodbury formula, to avoid large matrix inversion which results in a significant speedup. Experimental results with both noisy and noiseless data have shown that the proposed FRSMP method achieves a higher reconstruction accuracy at low reconstruction time compared to other greedy pursuit algorithms. Also, experiments on frequent face databases have proven that the KSRC-based FRSMP method shows reliable and higher recognition rate and computation time compared with other advanced sparse representation methods for face recognition.

Target Recognition of SAR Images Based on SVM and KSRC.

A synthetic aperture radar (SAR) target recognition method combining linear and nonlinear feature extraction and classifiers is proposed. The principal component analysis (PCA) and kernel PCA (KPCA) are used to extract feature vectors of the original SAR image, respectively, which are classical and reliable feature extraction algorithms. In addition, KPCA can effectively make up for the weak linear description ability of PCA. Afterwards, support vector machine (SVM) and kernel sparse representation-based classification (KSRC) are used to classify the KPCA and PCA feature vectors, respectively. Similar to the idea of feature extraction, KSRC mainly introduces kernel functions to improve the processing and classification capabilities of nonlinear data. Through the combination of linear and nonlinear features and classifiers, the internal data structure of SAR images and the correspondence between test and training samples can be better investigated. In the experiment, the performance of the proposed method is tested based on the MSTAR dataset. The results show the effectiveness and robustness of the proposed method.

An adaptive kernel sparse representation-based classification

In recent years, scholars have attached increasing attention to sparse representation. Based on compressed sensing and machine learning, sparse representation-based classification (SRC) has been extensively in classification. However, SRC is not suitable for samples with non-linear structures which arise in many practical applications. Meanwhile, sparsity is overemphasized by SRC, but the correlation information which is of great importance in classification is overlooked. To address these shortcomings, this study puts forward an adaptive kernel sparse representation-based classification (AKSRC). First, the samples were mapped to a high-dimensional feature space from the original feature space. Second, after selecting a suitable kernel function, a sample is represented as the linear combination of training samples of same class. Further more, the trace norm is adopted in AKSRC which is different from general approaches. It’s adaptive to the structure of dictionary which means that a better linear representation which has the most discriminative samples can be obtained. Therefore, AKSRC has more powerful classification ability. Finally, the advancement and effectiveness of the proposed AKSRC are verified by carrying out experiments on benchmark data sets.

Fast kernel sparse representation based classification for Undersampling problem in face recognition

We propose a fast kernel sparse representation based classification (SRC) for undersampling problem, i.e., each class has very few training samples, in face recognition. The proposed algorithm exploits a nonlinear mapping to map the data from the original input space into a high-dimensional feature space. Then, it performs very fast sparse representation and classification of samples in this space. Similar to the typical SRC methods, the proposed approach is based on the L1 norm minimization, whose direct solution can be very time-consuming. In order to improve the computational efficiency, our method uses the coordinate descent method in the feature space, which can avoid directly solving the L1 norm minimization problem, and significantly expedites the computational procedure. Compared with other SRC methods based on the L1 norm minimization, our proposed method achieves very high computational efficiency, without significantly degrading the classification performance. Several experiments on popular face databases demonstrate that our method is a promising efficient kernel SRC based method.

A novel face recognition algorithm via weighted kernel sparse representation

Face recognition with Kernel Sparse Representation based Classification (KSRC) has shown its great classification performance, and as an extension of Sparse Representation based Classifier (SRC), KSRC resolved the problem of nonlinear distribution of face images. However, the locality structure of image data contains more discriminative information which is essential for classification that does not be considered by KSRC. This paper proposes a novel face recognition algorithm called Weighted Kernel Sparse Representation based Classification (WKSRC). Firstly, each face image is mapped into kernel feature space with a kernel function, and dimensionality reduction method is applied to the feature space. And then, the matrix which denotes the similarity between the testing and training samples is obtained by Multiscale Retinex (MSR), which could reduce the influence of the illumination variations. Finally, the sparse coefficients for the testing sample are solved by optimization method and the classification result is obtained by minimizing the error between the original and reconstructed samples. The experiment results prove that the proposed WKSRC significantly improves the performance of face recognition compared with the existing algorithms. Moreover, the robustness to various illuminations and occlusions is also demonstrated, which proves the universality of our proposal.

3D model classification via Principal Thickness Images

With the innovation in 3D modeling software, more and more 3D models are becoming available in recent decades. To facilitate efficient retrieval and search of large 3D model databases, an effective shape classification algorithm is badly in need. In this paper, we propose a new feature descriptor named Principal Thickness Images (PTI) that encodes the boundary surface and the voxelized constituents of a 3D shape into three gray-scale images. With the support of PTI, we extend the kernel sparse representation-based classification from 2D case to non-rigid 3D models. Our classification algorithm inherits the robustness of kernel sparse representation and is able to achieve a high success rate and strong reliability on non-rigid models from the SHREC’11 non-rigid 3D models dataset. Numerous tests demonstrate superior performance of the proposed method over previous 3D shape classification approaches.

Classification on the monogenic scale space: application to target recognition in SAR image.

This paper introduces a novel classification strategy based on the monogenic scale space for target recognition in Synthetic Aperture Radar (SAR) image. The proposed method exploits monogenic signal theory, a multidimensional generalization of the analytic signal, to capture the characteristics of SAR image, e.g., broad spectral information and simultaneous spatial localization. The components derived from the monogenic signal at different scales are then applied into a recently developed framework, sparse representation-based classification (SRC). Moreover, to deal with the data set, whose target classes are not linearly separable, the classification via kernel combination is proposed, where the multiple components of the monogenic signal are jointly considered into a unifying framework for target recognition. The novelty of this paper comes from: the development of monogenic feature via uniformly downsampling, normalization, and concatenation of the components at various scales; the development of score-level fusion for SRCs; and the development of composite kernel learning for classification. In particular, the comparative experimental studies under nonliteral operating conditions, e.g., structural modifications, random noise corruption, and variations in depression angle, are performed. The comparative experimental studies of various algorithms, including the linear support vector machine and the kernel version, the SRC and the variants, kernel SRC, kernel linear representation, and sparse representation of monogenic signal, are performed too. The feasibility of the proposed method has been successfully verified using Moving and Stationary Target Acquiration and Recognition database. The experimental results demonstrate that significant improvement for recognition accuracy can be achieved by the proposed method in comparison with the baseline algorithms.

Use of customizing kernel sparse representation for hyperspectral image classification.

Sparse representation-based classification (SRC) has attracted increasing attention in remote-sensed hyperspectral communities for its competitive performance with available classification algorithms. Kernel sparse representation-based classification (KSRC) is a nonlinear extension of SRC, which makes pixels from different classes linearly separable. However, KSRC only considers projecting data from original space into feature space with a predefined parameter, without integrating a priori domain knowledge, such as the contribution from different spectral features. In this study, customizing kernel sparse representation-based classification (CKSRC) is proposed by incorporating kth nearest neighbor density as a weighting scheme in traditional kernels. Analyses were conducted on two publicly available data sets. In comparison with other classification algorithms, the proposed CKSRC further increases the overall classification accuracy and presents robust classification results with different selections of training samples.

Spoken emotion recognition via locality-constrained kernel sparse representation

Spoken emotion recognition is currently a very active research topic and has attracted extensive attention in signal processing, pattern recognition, artificial intelligence, etc. In this paper, a new emotion classification method based on kernel sparse representation, named locality-constrained kernel sparse representation-based classification (LC-KSRC), is proposed for spoken emotion recognition. LC-KSRC is able to learn more discriminating sparse representation coefficients for spoken emotion recognition, since it integrates both sparsity and data locality in the kernel feature space. The proposed method is compared with six representative emotion classification methods, including linear discriminant classifier, K-nearest-neighbor, radial basis function neural networks, support vector machines, sparse representation-based classification and kernel sparse representation-based classification. Experimental results on two publicly available emotional speech databases, i.e., the Berlin database and the Polish database, demonstrate the promising performance of the proposed method on spoken emotion recognition tasks, outperforming the other used methods.

Multiple kernel learning for sparse representation-based classification.

In this paper, we propose a multiple kernel learning (MKL) algorithm that is based on the sparse representation-based classification (SRC) method. Taking advantage of the nonlinear kernel SRC in efficiently representing the nonlinearities in the high-dimensional feature space, we propose an MKL method based on the kernel alignment criteria. Our method uses a two step training method to learn the kernel weights and sparse codes. At each iteration, the sparse codes are updated first while fixing the kernel mixing coefficients, and then the kernel mixing coefficients are updated while fixing the sparse codes. These two steps are repeated until a stopping criteria is met. The effectiveness of the proposed method is demonstrated using several publicly available image classification databases and it is shown that this method can perform significantly better than many competitive image classification algorithms.

EPILEPTIC EEG CLASSIFICATION BASED ON KERNEL SPARSE REPRESENTATION

The automatic identification of epileptic EEG signals is significant in both relieving heavy workload of visual inspection of EEG recordings and treatment of epilepsy. This paper presents a novel method based on the theory of sparse representation to identify epileptic EEGs. At first, the raw EEG epochs are preprocessed via Gaussian low pass filtering and differential operation. Then, in the scheme of sparse representation based classification (SRC), a test EEG sample is sparsely represented on the training set by solving l1-minimization problem, and the represented residuals associated with ictal and interictal training samples are computed. The test EEG sample is categorized as the class that yields the minimum represented residual. So unlike the conventional EEG classification methods, the choice and calculation of EEG features are avoided in the proposed framework. Moreover, the kernel trick is employed to generate a kernel version of the SRC method for improving the separability between ictal and interictal classes. The satisfactory recognition accuracy of 98.63% for ictal and interictal EEG classification and for ictal and normal EEG classification has been achieved by the kernel SRC. In addition, the fast speed makes the kernel SRC suit for the real-time seizure monitoring application in the near future.

Kernel sparse representation with pixel-level and region-level local feature kernels for face recognition

Face recognition has been popular in the pattern recognition field for decades, but it is still a difficult problem due to the various image distortions. Recently, sparse representation based classification (SRC) was proposed as a novel image classification approach, which is very effective with sufficient training samples for each class. However, the performance drops when the number of training samples is limited. In this paper, we show that effective local image features and appropriate nonlinear kernels are needed in deriving a better classification method based on sparse representation. Thus, we propose a novel kernel SRC framework and utilize effective local image features in this framework for robust face recognition. First, we present a kernel coordinate descent (KCD) algorithm for the LASSO problem in the kernel space, and we successfully integrate it in the SRC framework (called KCD-SRC) for face recognition. Second, we employ local image features and develop both pixel-level and region-level kernels for KCD-SRC based face recognition, making it discriminative and robust against illumination variations and occlusions. Extensive experiments are conducted on three public face databases (Extended YaleB, CMU-PIE and AR) under illumination variations, noise corruptions, continuous occlusions, and registration errors, demonstrating excellent performances of the KCD-SRC algorithm combining with the proposed kernels.

Class-Discriminative Kernel Sparse Representation-Based Classification Using Multi-Objective Optimization

In this paper, we propose class-discriminative kernel sparse representation-based classification (KSRC) using multi-objective optimization (MOO) called KSRC 2.0. In sparse representation-based classification (SRC), both dictionary and residuals (reconstruction errors) play an important role in classifying a sample. Thus, discriminative dictionary and residuals are required to achieve high classification performance. To generate discriminative dictionary and residuals from training data sets, we formulate multi-objective functions via the Fisher discrimination criterion that minimizes distances within and maximizes distances between classes. Then, we solve them by using MOO, which can optimize conflicting objectives at the same time, and obtain component importance factors to make dictionary and residuals class-discriminative. Extensive experiments on publicly available databases demonstrate that the proposed KSRC 2.0 enhances the class separability of KSRC and achieves high classification performance.

Kernel sparse representation based classification

Sparse representation has attracted great attention in the past few years. Sparse representation based classification (SRC) algorithm was developed and successfully used for classification. In this paper, a kernel sparse representation based classification (KSRC) algorithm is proposed. Samples are mapped into a high dimensional feature space first and then SRC is performed in this new feature space by utilizing kernel trick. Since samples in the high dimensional feature space are unknown, we cannot perform KSRC directly. In order to overcome this difficulty, we give the method to solve the problem of sparse representation in the high dimensional feature space. If an appropriate kernel is selected, in the high dimensional feature space, a test sample is probably represented as the linear combination of training samples of the same class more accurately. Therefore, KSRC has more powerful classification ability than SRC. Experiments of face recognition, palmprint recognition and finger-knuckle-print recognition demonstrate the effectiveness of KSRC.