Sparse discriminative feature selection

doi:10.1016/j.patcog.2014.10.021

Sparse discriminative feature selection

2015-05-01

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DOI：10.1016/j.patcog.2014.10.021

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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 l(2,1)-norm 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. (C) 2014 Elsevier Ltd. All rights reserved.

Keywords:

Joint feature selection

Sparse representation based classification

Discriminative learning

Sparse discriminative feature selection

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