Double competitive constraints-based collaborative representation for pattern classification

Representation-based classification (RBC) has attracted much attention in pattern recognition. As a linear representative RBC method, collaborative representation-based classification (CRC) is very promising for classification. Although many extensions of CRC have been developed recently, the discriminative and competitive representations of different classes for favorable classification has not yet been fully explored. To design the discriminative and competitive collaborative representations for enhancing the power of pattern discrimination, we propose a novel double competitive constraints-based collaborative representation for classification (DCCRC). In the proposed DCCRC, one competitive constraint is the l2-norm regularization of residuals between each query sample and the class-specific representations, the other one is the l2-norm regularization of the representations of all the classes excluding any one class. In two competitive constraints, the class discrimination information is employed to generate competitive representations. Moreover, the proposed method integrates both the representation learning and classification into the unified model. We study the effectiveness and robustness of the proposed method by comparing it with the state-of-the-art CRC methods on six face databases and twelve UCI data sets. The experimental results demonstrate the promising classification performance of the proposed method.