Sparse neighbor representation for classification

Abstract

Recent research of sparse signal representation has aimed at learning discriminative sparse models instead of purely reconstructive ones for classification tasks, such as sparse representation based classification (SRC) which obtains state-of-the-art results in face recognition. In this paper, a new method is proposed in that direction. With the assumption of locally linear embedding, the proposed method achieves the classification goal via sparse neighbor representation, combining the reconstruction property, sparsity and discrimination power. The experiments on several data sets are performed and results show that the proposed method is acceptable for nonlinear data sets. Further, it is argued that the proposed method is well suited for the classification of low dimensional data dimensionally reduced by dimensionality reduction methods, especially the methods obtaining the low dimensional and neighborhood preserving embeddings, and it costs less time.