Sparse discriminative feature weights learning

Abstract

Sparse representation, a locality-based data representation method, leads to promising results in many scientific and engineering fields. Meanwhile in the study of feature selection, locality preserving is widely recognized as an effective measurement criterion. In this paper, we introduce l1-norm driven sparse representation into feature selection, and propose a novel joint feature weights learning algorithm, named sparse discriminative feature weights (SDFW). SDFW assigns the highest score to the feature that has the smallest difference between within-class reconstruction residual and between-class reconstruction residual in the space of selected features. It possesses the following advantages: (1) compared with feature selection methods based on k nearest neighbors, SDFW automatically (vs. manually) determines neighborhood for individual sample; (2) compared with conventional heuristic feature search which selects features individually, SDFW selects feature subset in batch mode. Extensive experiments on different data types demonstrate the effectiveness of SDFW.