基于多重核稀疏表示分类

Sparse representation based classification (SRC) and kernel methods have been successfully applied in many pattern recognition problems. In order to improve the classification accuracy, we propose multiple kernel sparse representation based classification (MKSRC). A fast optimization iteration method for solving sparse coefficients and the proof of the associated convergence to global optimal solution are given. For updating the kernel weights of MKSRC, two different updating methods and the associated comparison and analysis are given. The experimental results on four face image databases show the superiority of the proposed multiple kernel sparse representation based classification.