Local Sparse Representation Based Classification

In this paper, we address the computational complexity issue in Sparse Representation based Classification (SRC). In SRC, it is time consuming to find a global sparse representation. To remedy this deficiency, we propose a Local Sparse Representation based Classification (LSRC) scheme, which performs sparse decomposition in local neighborhood. In LSRC, instead of solving the l₁-norm constrained least square problem for all of training samples we solve a similar problem in a local neighborhood for each test sample. Experiments on face recognition data sets ORL and Extended Yale B demonstrated that the proposed LSRC algorithm can reduce the computational complexity and remain the comparative classification accuracy and robustness.