Simultaneous dimensionality reduction and dictionary learning for sparse representation based classification

Learning dictionaries from the training data has led to promising results for pattern classification tasks. Dimensionality reduction is also an important issue for pattern classification. However, most existing methods perform dimensionality reduction (DR) and dictionary learning (DL) independently, which may result in not fully exploiting the discriminative information of the training data. In this paper, we propose a simultaneous dimensionality reduction and dictionary learning (SDRDL) model to learn a DR projection matrix and a class-specific dictionary (i.e., the dictionary atoms correspond to the class labels) simultaneously. Since simultaneously learning makes the learned projection and dictionary fit better with each other, more effective pattern classification can be achieved using the representation residual. In SDRDL model, not only the representation residual is discriminative, but the representation coefficients are also discriminative. Therefore, a classification scheme associated with SDRDL is presented by exploiting such discriminative information. Experimental results on a series of benchmark image databases show that our proposed method outperforms many state-of-the-art discriminative dictionary learning methods.