Joint sparse representation and locality preserving projection for feature extraction

Traditional graph-based feature extraction methods use two separated procedures, i.e., graph learning and projection learning to perform feature extraction. They make the feature extraction result highly dependent on the quality of the initial fixed graph, while the graph may not be the optimal one for feature extraction. In this paper, we propose a novel unsupervised feature extraction method, i.e., joint sparse representation and locality preserving projection (JSRLPP), in which the graph construction and feature extraction are simultaneously carried out. Specifically, we adaptively learn the similarity matrix by sparse representation, and at the same time, learn the projection matrix by preserving local structure. Compared with traditional feature extraction methods, our approach unifies graph learning and projection learning to a common framework, thus learns a more suitable graph for feature extraction. Experiments on several public image data sets demonstrate the effectiveness of our proposed algorithm.