Sparse discriminative feature selection. Issue 5 (May 2015) - Yan, Hui; Yang, Jian
Abstract As sparse representation-based classifier (SRC) is developed, it has drawn more and more attentions in dimension reduction. In this paper, we introduce SRC based measurement criterion into feature selection, and then propose a novel method called sparse discriminative feature selection. Our objective function aims to find a subset of features, which minimize the within-class reconstruction residual and simultaneously maximize the between-class reconstruction residual in the subspace of selected features. A greedy algorithm and a joint selection algorithm are devised to efficiently solve the proposed combinatorial optimization formulation. In particular, our joint selection algorithm adds <alternatives><inline-graphic xlink:href="ark:/27927/pgh3mhfswct" xlink:type="simple" xmlns:xlink="http://www.w3.org/1999/xlink" />l2, 1-norm</alternatives> minimization into the objective function, which reduces the redundant and learns features weights simultaneously. A new iterative algorithm is also developed to optimize the proposed objective function. Experiments on benchmark data sets demonstrate the performance of our feature selection method.