Laplacian Regularized Non-negative Sparse Low-Rank Representation Classification

Jingshan Li, Caikou Chen, Xielian Hou, Rong Wang

Abstract: Recently low-rank becomes a popular tool for face representation and classification. None of these existing low-rank based classification methods are in view of the non-linear geometric structures within data, hence the data during the learning process may lose locality and similarity information. Furthermore, Lin et al. propose a Non-negative Sparse Hyper-Laplacian regularized LRR model (NSHLRR) to improve LRR in the above respect and apply it to image clustering. In this paper, we propose a novel classification method, namely NSHLRR-based Classification (NSHLRRC) for face recognition. Experimental results on public face databases clearly show our method has very competitive classification results, which also show that our method outperforms other state-of-the-art methods.