Learning Structured Dictionary Based on Inter-Class Similarity and Representative Margins

Abstract

We consider the problem of learning a structured and discriminative dictionary based on sparse representation for classification task. The structure comprises class-shared and class-specific partitions which allows the separation of common and class-specific information in the data for classification. The resulting optimization problem was a max margin formulation that exploits the hinge loss function property. Comparative evaluation of the proposed classifier against four recent alternatives in a gender classification task indicates a 3-percenatge point improvement.