Discriminative sparsity preserving projections for image recognition

Previous works have demonstrated that image classification performance can be significantly improved by manifold learning. However, performance of manifold learning heavily depends on the manual selection of parameters, resulting in bad adaptability in real-world applications. In this paper, we propose a new dimensionality reduction method called discriminative sparsity preserving projections (DSPP). Different from the existing sparse subspace algorithms, which manually construct a penalty adjacency graph, DSPP employs sparse representation model to adaptively build both intrinsic adjacency graph and penalty graph with weight matrix, and then integrates global within-class structure into the discriminant manifold learning objective function for dimensionality reduction. Extensive experimental results on four image databases demonstrate the effectiveness of the proposed approach.