基于多重核的稀疏表示分类

摘要

稀疏表示分类（SRC ）及核方法在模式识别的很多问题中都得到了成功的运用。为了提高其分类精度，提出多重核稀疏表示及其分类（MKSRC ）方法。提出一种快速求解稀疏系数的优化迭代方法并给出了其收敛到全局最优解的证明。对于多重核的权重给出了两种自动更新方式并进行了分析与比较。在不同的人脸图像库上的分类实验显示了所提出的多重核稀疏表示分类的优越性。%Sparse representation based classification (SRC) and kernel methods are applied in many pattern recognition prob-lems .In order to improve the classification accuracy ,we propose multiple kernel sparse representation based classification (MK-SRC) .A fast optimization iteration method to solve sparse coefficients and the associated convergence proof to global optimal solu-tion are given .In order to update the kernel weights of MKSRC ,two different updating methods and the associated comparison are given .The experimental results on three face image databases show the superiority of the proposed multiple kernel sparse representa-tion based classification .