Kernel Sparse Representation-based Classifier Research Articles

Identification of DNA N4-methylcytosine Sites via Multiview Kernel Sparse Representation Model

Identifying DNA N4-methylcytosine (4mC) sites is of great significance in biological research, such as chromatin structure, DNA stability, DNA-protein interaction and controlling gene expression. However, the traditional sequencing technology to identify 4mC sites is very time-consuming. In order to detect 4mC sites, we develop a multi-view learning method for achieving more effectively via merging multiple feature spaces. Furthermore, we think about whether the multi-view learning method can improve the across species classification ability by fusing data of multiple species. In our study, we propose a multi-view Laplacian kernel sparse representation-based classifier, called MvLapKSRC-HSIC. First, we make use of three feature extraction methods (PSTNP, NCP, DPP) to extract the DNA sequence features. MvLapKSRC-HSIC uses a kernel sparse representation-based classifier with graph regularization. In order to maintain the independence between various views, we add a multi-view regularization term constructed by Hilbert-Schmidt independence criterion (HSIC). In the experiments, MvLapKSRC-HSIC is applied on six datasets, so as to compare with other popular methods in single species and cross-species experiments. All experimental results show that MvLapKSRC-HSIC is superior to other outstanding methods on both single species and cross-species. Importantly, MvLapKSRC-HSIC can identify a series of potential DNA 4mC sites, which have not yet been experimentally evaluate on multiple species and merit further research.

Hyperspectral Image Classification Using Kernel Fused Representation via a Spatial-Spectral Composite Kernel With Ideal Regularization

To adequately exploit spectral, spatial, and label information of the given hyperspectral data, a kernel fused representation-based classifier via a spatial-spectral composite kernel with ideal regularization (CKIR) method is proposed in this letter. Specifically, the learned CKIR is embedded into the kernel version of representation-based classifiers, i.e., kernel sparse representation-based classifier (KSRC) and kernel collaborative representation-based classifier (KCRC), to obtain more discriminative representation coefficients. Furthermore, to benefit from both sparsity and data correlation in representation, KSRC and KCRC are combined in the CKIR-based residual domain to further enhance the discriminative ability of the proposed classifier. The experimental results on two real hyperspectral images demonstrate that the proposed method outperforms the other state-of-the-art classifiers.

An Object Recognition Approach for Synthetic Aperture Radar Images

In this paper, an object recognition approach for synthetic aperture radar (SAR) images is addressed, which is based on the enhanced kernel sparse representation of monogenic signal. It consists of two main modules. In the first module, to capture the spatial and spectral properties of a target at the same time, a multi-scale monogenic feature extraction scheme is proposed. In the second module, an enhanced kernel sparse representation-based classifier (KSRC) is designed. Different from the traditional KSRC, in the enhanced KSRC, we first integrate the kernel principal component analysis (KPCA) as well as the kernel fisher discriminant analysis (KFDA) to generate an augmented pseudo-transformation matrix. Then, a new discriminative feature mapping approach is presented by exploiting the augmented pseudo-transformation matrix so that the dimensionality of the kernel feature space can be effectively reduced. At last, the l1 -norm minimization is utilized to calculate the sparse coefficients for a test sample, and thus the inference can be reached in terms of the total reconstruction error. Experimental results on the public moving and stationary target acquisition and recognition dataset (MSTAR) demonstrate that the proposed method achieves high recognition accuracy for SAR automatic target recognition.

Multiple Feature Kernel Sparse Representation Classifier for Hyperspectral Imagery

Multiple types of features, e.g., spectral, filtering, texture, and shape features, are helpful for hyperspectral image (HSI) classification tasks. Combining multiple features can describe the characteristics of pixels from different perspectives, and always results in better classification performance. Recently, multifeature combination learning has been widely employed to the multitask-learning-based representation-based model to obtain a multifeature representation vector. However, the linear sparse representation-based classifier (SRC) cannot handle the HSI with highly nonlinear distribution, and kernel sparse representation-based classifier (KSRC) can remedy the drawback of linear SRC. By adopting nonlinear mapping, the samples in kernel space are often of high or even infinite dimensionality. In this paper, we integrate kernel principal component analysis into multifeature-based KSRC and propose a novel multiple feature kernel sparse representation-based classifier (namely, MFKSRC) for hyperspectral imagery. More specifically, spatial features, Gabor textures, local binary patterns, and difference morphological profiles are adopted and then each kind of feature is transformed nonlinearly into a new low-dimensional kernel space. The proposed framework can handle data with nonlinear distribution and add a dimensionality reduction stage in kernel space before optimizing the corresponding cost function. Experimental results on different HSIs demonstrate that the proposed MFKSRC algorithm outperforms the state-of-the-art classifiers.

Class-Oriented Weighted Kernel Sparse Representation With Region-Level Kernel for Hyperspectral Imagery Classification

As a nonlinear extension of traditional sparse representation-based classifier (SRC), kernel SRC (KSRC) has shown its excellent performance for hyperspectral image (HSI) classification, by mapping the nonlinearly separable samples into high-dimensional feature space. However, the rich locality structure of HSI contains more discriminative information, which should be considered in KSRC. We intend to incorporate the locality structure and kernel method into a unified SR-based framework by a local spatial kernel. As a powerful texture descriptor, local binary patterns (LBP) was used to extract local feature for remote sensing. Region-level kernels are applied to calculate the distance between two LBP histogram features. To discover nonlinear similarity information between test and training samples, we integrate the LBP feature into spatial region-level kernel for HSI classification. Then, we propose a weighted kernel sparse representation classifier optimized via class-oriented strategy, which combines local structure information and SRC in the kernel feature space based on spatial region-level kernel. Experimental results on three open HSIs demonstrate that the proposed method achieves better classification performance than other state-of-the-art classification methods.

Nonparametric kernel sparse representation-based classifier

Sparse representation-based classifier (SRC) and kernel sparse representation-based classifier (KSRC) are founded on combining pattern recognition and compressive sensing methods and provide acceptable results in many machine learning problems. Nevertheless, these classifiers suffer from some shortcomings. For instance, SRC's accuracy drops against samples from same directional classes or KSRC's output declines when data is not normally distributed in kernel space. This paper introduces nonparametric kernel sparse representation-based classifier (NKSRC) as a generalized framework for SRC and KSRC. First, it applies kernel on samples to overcome data directionality and then employs nonparametric discriminant analysis (NDA) to reduce data dimensionality in kernel space alleviating concern about data distribution type. The experimental results of NKSRC demonstrate its superiority over SRC and KSRC–LDA and its equal or superior performance with respect to KSRC–PCA on some synthetic, four well-known face recognition and several UCI datasets.

Multiple Kernel Sparse Representation-Based Orthogonal Discriminative Projection and Its Cost-Sensitive Extension.

Sparse representation-based classification (SRC) has been developed and shown great potential for real-world application. Based on SRC, Yang et al. devised an SRC steered discriminative projection (SRC-DP) method. However, as a linear algorithm, SRC-DP cannot handle the data with highly nonlinear distribution. Kernel sparse representation-based classifier (KSRC) is a non-linear extension of SRC and can remedy the drawback of SRC. KSRC requires the use of a predetermined kernel function and selection of the kernel function and its parameters is difficult. Recently, multiple kernel learning for SRC (MKL-SRC) has been proposed to learn a kernel from a set of base kernels. However, MKL-SRC only considers the within-class reconstruction residual while ignoring the between-class relationship, when learning the kernel weights. In this paper, we propose a novel multiple kernel sparse representation-based classifier, and then we use it as a criterion to design a multiple kernel sparse representation-based orthogonal discriminative projection method. The proposed algorithm aims at learning a projection matrix and a corresponding kernel from the given base kernels such that in the low dimension subspace the between-class reconstruction residual is maximized and the within-class reconstruction residual is minimized. Furthermore, to achieve a minimum overall loss by performing recognition in the learned low-dimensional subspace, we introduce cost information into the dimensionality reduction method. The solutions for the proposed method can be efficiently found based on trace ratio optimization method. Extensive experimental results demonstrate the superiority of the proposed algorithm when compared with the state-of-the-art methods.

Kernel sparse representation-based classifier ensemble for face recognition

Kernel sparse representation-based classifier (KSRC) has been proposed, which has good representation and classification performance on face image data. The performance of KSRC on face image data is partly dependent on the random projection matrix when using the random projection method and the kernel Gram matrix. This paper develops the kernel sparse representation-based classifier ensemble (KSRCE), which does not require to consider the effect of random projection and kernel Gram matrix on KSRC. Actually, the random projection matrix and the kernel Gram matrix could be used for designing the diversity schemes for KSRCE. In the combination stage, we can combine the labels or the reconstruction errors of a test sample. Experimental results on three face data sets show that KSRCE is very promising.

Kernel Sparse Representation-Based Classifier

Sparse representation-based classifier (SRC), a combined result of machine learning and compressed sensing, shows its good classification performance on face image data. However, SRC could not well classify the data with the same direction distribution. The same direction distribution means that the sample vectors belonging to different classes distribute on the same vector direction. This paper presents a new classifier, kernel sparse representation-based classifier (KSRC), based on SRC and the kernel trick which is a usual technique in machine learning. KSRC is a nonlinear extension of SRC and can remedy the drawback of SRC. To make the data in an input space separable, we implicitly map these data into a high-dimensional kernel feature space by using some nonlinear mapping associated with a kernel function. Since this kernel feature space has a very high (or possibly infinite) dimensionality, or is unknown, we have to avoid working in this space explicitly. Fortunately, we can indeed reduce the dimensionality of the kernel feature space by exploiting kernel-based dimensionality reduction methods. In the reduced subspace, we need to find sparse combination coefficients for a test sample and assign a class label to it. Similar to SRC, KSRC is also cast into an ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -minimization problem or a quadratically constrained ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -minimization problem. Extensive experimental results on UCI and face data sets show KSRC improves the performance of SRC.