Sparse Representation Classifier Steered Discriminative Projection

The sparse representation-based classifier (SRC) has been developed and shows great potential for pattern classification. This paper aims to gain a discriminative projection such that SRC achieves the optimum performance in the projected pattern space. We use the decision rule of SRC to steer the design of a dimensionality reduction method, which is coined the sparse representation classifier steered discriminative projection (SRC-DP). SRC-DP matches SRC optimally in theory. Experiments are done on the AR and extended Yale B face image databases, and results show the proposed method is more effective than other dimensionality reduction methods with respect to the sparse representation-based classifier.