Discrimination projective dictionary pair methods in dictionary learning1

Recently, the projective dictionary pair learning (DPL) method has obtained better classification representation accuracy which learned a synthesis dictionary and an analysis dictionary to achieve the goal of signal representation. In order to obtain more discriminative ability of the dictionary pair, a new method based on DPL, called discrimination projective dictionary pair learning based dictionary learning method (DPDPL), will be proposed. In DPDPL, we will consider both the inter-class and intra-class incoherence constraints of the synthesis dictionary, and the analysis dictionary should be used to simultaneously maximize the total scatter and the between-class scatter of the signal after coding. Thus, the method ensures that the dictionary has better discriminative ability and the signals are more separable after coding. Experiments of sparse representation-based classification on several face databases show the good performances of the proposed dictionary learning method.