Discriminative Representative Selection via Structure Sparsity

This paper focuses on the problem of finding a few representatives for a given dataset, which have both representation and discrimination ability. To solve this problem, we propose a novel algorithm, called Structure Sparsity based Discriminative Representative Selection (SSDRS), to find a representative subset of data points. The selected representative subset keeps the representation ability based on sparse representation models assuming that each data point can be expressed as a linear combination of those representatives. Meanwhile, we employ the Fisher discrimination criterion to make the coefficient matrix possess small within-class scatter but big between-class scatter, which leads to the discriminant ability of representatives. Since such a selected subset is representative and discriminative, it can be used to properly describe the entire dataset and achieve a good classification performance simultaneously. Experimental results in terms of video summarization and image classification indicate that our proposed algorithm outperforms the state-of-the-art methods.