Similarity weighted sparse representation for classification

Song Guo, Qiuqi Ruan, Zhenjiang Miao

Abstract: In this paper, we propose a novel sparse representation method for classification called similarity weighted sparse representation (SWSR). The similarity weighted ℓ1-norm minimization, where the weighted matrix is constructed by incorporating the similarity information between the test sample and the entire training samples, is presented as an alternative to ℓ0-norm minimization to seek the optimal sparse representation for the test sample in SWSR. The sparse solution of SWSR encodes more discriminative information than other competing alternatives to ℓ0-norm minimization, so it is more suitable for classification. The experimental results on publicly available face databases demonstrate the efficacy of the proposed method.