Concentrative sparse representation based classification

Sparse representation is being proved to be effective for many tasks in the field of pattern recognition. In this paper, an efficient classification algorithm based on concentrative sparse representation will be proposed to address the problem caused by insufficient training samples in each class. We firstly compute representation coefficient of the testing sample with training samples matrix using subspace pursuit recovery algorithm. Then we define concentration measurement function in order to determine whether the sparse representation coefficient is concentrative. Subspace pursuit is repeatedly used to revise the sparse representation until concentration is met. Such a concentrative sparse representation can contribute to discriminative residuals that are critical to accurate classification. The experimental results have showed that the proposed algorithm achieves a satisfying performance in both accuracy and efficiency.