Learning Low-Rank Kernel Matrices with Column-Based Methods

We consider the problem of computing a low-rank approximation for large kernel matrix. In this article, a novel strategy called two-stage low-rank kernel matrix selection is proposed for computational efficiency enhancement. Firstly, two permutation sets are obtained by a proposed hybrid column-based selection method, which leads to significant reduction of kernel matrix in size. Secondly, entries of the resultant matrix are selected using information theoretic learning. Then this matrix is used for classification. Experimental results on real data sets have shown the superiority of the proposed method in terms of computational efficiency and classification accuracy, especially when training samples size is large.