Fast Kernel Sparse Representation Classifier using Improved Smoothed-l0 Norm

The computation time for solving classification problem using sparse representation classifier remains a huge drawback as it is to be implemented in real time applications. The time consuming of sparse representation classifier is mainly due to the sparse signal recovery solver which is based on l(1) minimization or Basis Pursuit. Since then, a fast version of sparse signal recovery solver is introduced and it is based on smoothing the discontinuous properties of l(0) norm. In this work, a smoothed l(0) norm solver is implemented in sparse representation classifier algorithm. This smoothed l(0) norm solver is also modified and improved in such a way to increase its classification accuracy and to further reduce the computation time. The use of kernel version of sparse representation classifier to this modified solver is also implemented and described in this paper. Experiments based on human speech data are carried out in order to compare the improved version of sparse representation classifier with the state of the art classifiers. Experimental results prove that the computation time for classification using proposed algorithm is greatly reduced compared to the baseline performances. (C) 2017 The Authors. Published by Elsevier B.V.