Sparse discriminative feature selection

作者：Yan, Hui^*; Yang, Jian

来源：Pattern Recognition, 2015, 48(5): 1827-1835.

DOI：10.1016/j.patcog.2014.10.021

• Joint feature selection
• Sparse representation based classification
• Discriminative learning

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.

• 出版日期2015-5
• 单位南京理工大学