Sparsity Regularization Discriminant Projection for Feature Extraction

Recently, sparse representation models have attracted considerable interests in the field of feature extraction. In this paper, we propose a novel supervised feature extraction method called sparsity regularization discriminant projection (SRDP), which aims to preserve the sparse representation structure of the data and simultaneously maximize the ratio of nonlocal scatter to local scatter. More specifically, SRDP first constructs a concatenated dictionary through the class-wise principal component analysis decompositions. Second, the sparse representation structure of each sample is quickly learned with the constructed dictionary by matrix–vector multiplications. Then SRDP regards the learned sparse representation structure as an additional regularization term of unsupervised discriminant projection so as to construct a new discriminant function. Finally, SRDP is transformed into a generalized eigenvalue problem. Experimental results on five representative image databases demonstrate the effectiveness of our proposed method.