Locality sensitive discriminative dictionary learning

Abstract

Discriminative dictionary learning (DDL) has been applied to various pattern classification problems. Despite satisfying experimental results, most existing discriminative dictionary learning methods emphasize too much on the role of l 0 or l 1 -norm sparsity, while the underlying local structure of original data is totally ignored. In this paper, we present a novel dictionary learning method, named Locality Sensitive Discriminative Dictionary Learning (LSDDL), which combines basic dictionary learning scheme and locality relationship of original data which is propagated to the coding vectors. The learned discriminative dictionary can map the original data points into a new space in which the nearby points with the same label are close to each other while the nearby points with different labels are far apart. Experiments clearly show that our method has very competitive performance in contrast to previous discriminative dictionary learning methods.