Greedy dictionary learning for kernel sparse representation based classifier

We present a novel dictionary learning (DL) approach for sparse representation based classification in kernel feature space. These sparse representations are obtained using dictionaries, which are learned using training exemplars that are mapped into a high-dimensional feature space using the kernel trick. However, the complexity of such approaches using kernel trick is a function of the number of training exemplars. Hence, the complexity increases for large datasets, since more training exemplars are required to get good performance for most of the pattern classification tasks. To address this, we propose a hierarchical DL approach which requires the kernel matrix to update the dictionary atoms only once. Further, in contrast to the existing methods, the dictionary is learned in a linearly transformed/coefficient space involving sparse matrices, rather than the kernel space. Compared to the existing state-of-the-art methods, the proposed method has much less computational complexity, but performs similar for various pattern classification tasks. (C) 2016 Elsevier B.V. All rights reserved.