Extensional Neighborhood Preserving Embedding

Abstract

In this paper, a modified construction method of neighbor-graph is proposed for Neighborhood Preserving Embedding (NPE). NPE aims to find the local structure of the data set manifold and it is a linear approximation to Locally Linear Embedding (LLE). Although NPE gets an optimal embedding which can preserve the neighborhood structure for the original data set, but the construction method of neighborgraph is not the best, which suffers from the following issue: 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 modify the neighbor-graph construction method to preserve the corresponding columns information. In our method, we can describe the intrinsic structure of original data in lowdimensional space much better. Extensive experiments are performed on the well-known face databases: AR, ORL and Yale face databases to test and evaluate our method’s performance. The result demonstrates the effectiveness of our new method.