Incoherent dictionary learning for sparse representation

Recent years have witnessed a growing interest in the sparse representation problem. Prior work demonstrated that adaptive dictionary learning techniques can greatly improve the performance of sparse representation approaches. Existing techniques mainly focus on the reconstructive accuracies and the discriminative power of the learned dictionary, whereas the mutual incoherence between any two basis atoms has been rarely studied yet. This paper proposes a novel method by explicitly incorporating a correlation penalty into the dictionary learning model. Experiments show that the proposed method can remarkably reduce the correlation measure of the learned dictionaries, and at the same time achieve higher classification accuracies than state-of-the-art algorithms.