Sparse discriminative feature selection. Issue 5 (May 2015)

• Record Type: Journal Article
• Authors: Yan, Hui; Yang, Jian
• Is Part Of: Pattern recognition. Volume 48:Issue 5(2015:May)
• Page Start: 1827
• Page End: 1835
• Publication Date: 2015-05
• Subjects: Joint feature selection -- Sparse representation based classification -- Discriminative learning; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2014.10.021
• Languages: English
• ISSNs: 0031-3203

Abstract

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 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. Abstract : Highlights: The proposed method selects features that can preserve the sparse reconstructive relationship of the data. A greedy algorithm and a joint selection algorithm are devised to efficiently solve the proposed formulation. We incorporate discriminative analysis and l2;1_norm minimization into a joint feature selection.