Kernel Block-Sparse Representation for Classification

Block-sparse representation based classifier (BSRC), an extension of sparse representation based classification (SRC), shows good performance for face recognition task with small amount of training data. However, BSRC does not handle the inter-block and intra-block non-linearities present in the input feature space. As most of the real world data is non-linear, this paper presents a non-linear extension of BSRC, kernel block-sparse representation based classifier (KBSRC), using the kernel trick. We transform the data non-linearity to a higher dimensional space. Two convex optimization problems based on block sparsity are solved in this kernel feature space. Dimensionality reduction technique in kernel feature space is also applied to avoid the increase in computational complexity of the algorithm. In the reduced kernel feature subspace, a test sample is assigned a label based on block-sparse coefficients. To validate the proposed approach, experimentation over Extended Yale B Face, ISOLET and MNIST datasets is performed and it shows significant improvement in classification performance with less training data than state-of-the-art methods.