A New Construction Method of Neighbor Graph for Locality Preserving Projections

Abstract

In this paper, a new construction method of neighbor graph is proposed for Locality Preserving Projections (LPP). LPP is a typical method of graph-based dimensionality reduction, and has been successfully applied in many practical problems such as face recognition. However, LPP mainly depends on its essential neighbor graph whose construction suffers from the following issues: the data set is vectorized to compute the k-nearest neighbor graph (adjacency graph), which leads to the lost of the correlative columns information. So, we propose a new neighbor graph construction method which can well show the spatial structure information of the original image matrices, to preserve the corresponding columns information. In order to test and evaluate our method’s performance, a series of experiments were performed on the well-known face databases: ORL and Yale face databases. The experimental results show that our method achieves better performance than LPP.