Locality preserving manifold learning algorithm based on neighborhood in feature space
基于特征子空间邻域的局部保持流形学习算法*

Locality preserving manifold learning algorithms always discover intrinsic manifold in high-dimensional data by preserving locality neighborhood structures.However,for high-dimensional data with non-enough training samples,or with nonlinear structure and redundant or interrupted features,it is difficult to directly estimate real neighbor relation defined by Euclidean distance in original feature space.This paper proposed a novel method to find a feature subspace best suited to representing neighborhood relation using positive constraints.In this subspace more inner-class samples come together.Further,constructed neighborhood graph in this subspace to discover intrinsic manifold in high-dimensional data,which caused novel locality preserving manifold learning algorithms called NFS-LPP and NFS-NPE.Experimental results on Yale and ORL face database verify their effectiveness.