Research On Neighborhood Preserving Embedding Technology

Face recognition is one of the most biggest concerns areas in biometric identification,and it is an active direction in artificial intelligence and pattern recognition.It has important value in real life,such as college sign system,video conference,monitoring system,file management system.Among them,feature extraction is a vital part of face recognition,which directly affects result.NPE is a subspace feature learning method that preserves local structure information of data.It is assumed that each data point and its neighbors are in a linear or approximate linear manifold structure.NPE find a projection matrix,so that the projected points still maintain this relationship.This paper aims to do a further research and discussion.The brief introduction are as follows:1.NPE preserves local neighborhood structure information by constructing adjacency graph in original space.The samples of same class in original space do not always have adjacent distribution because of noise,so NPE can not characterize the structure well.In addition,it learns weight matrix and projection matrix separately.To solve these problems,we propose INPE algorithm,which projects data into low-dimensional subspace and then constructs adjacent graph.It learns weight matrix and projection matrix simultaneously and describes more compactly and accurately,which can weaken the influence of noise and outliers and improve the effect in face recognition.2.INPE describes the neighborhood information more compactly,however,it chooses the neighborhood numbers manually.To solve these problems,we propose LRNPE algorithm,which combines LRR by adding the minimum rank constraint to weight matrix.It learns adaptive weight matrix and projection matrix simultaneously.LRNPE avoids the intervention of artificial parameters,describes global information well and is more robust to noise.Based on LRNPE,we propose SLRNPE algorithm which introduces the regular term related to label information through making the difference between between-class scatter matrix and within-class scatter matrix to largest.Thus,it preserves local structure information and discriminate information,which effectively improves classification effect.