Low-rank graph preserving discriminative dictionary learning for image recognition

Discriminative dictionary learning plays a key role in sparse representation-based classification. In this paper, we propose a low-rank graph preserving discriminative dictionary learning (LRGPDDL) method for sparse representation-based image recognition. Specifically, we learn a common sub-dictionary as well as several class-specific sub-dictionaries to explicitly capture the common information shared by all the classes and the class-specific information belonging to the corresponding class. We also impose a low-rank constraint on each sub-dictionary to weaken the negative influence from noise contained in training samples. A discriminative graph preserving criterion and a discriminative reconstruction error term are used for exploiting discriminative information, which can improve the discriminative ability of the learned dictionary effectively. In addition, an incoherence term is also introduced into the proposed dictionary learning model to encourage the learned sub-dictionaries to be as independent as possible. Experimental results on several image datasets verify the effectiveness and robustness of LRGPDDL. •A low-rank graph preserving discriminative dictionary learning method is proposed.•A low-rank constraint and an incoherence term are introduced in the DL model.•A discriminative graph preserving criterion is incorporated into the DL model.