%0 Journal Article
%T Locality preserving manifold learning algorithm based on neighborhood in feature space<br>基于特征子空间邻域的局部保持流形学习算法*
%A WANG N
%A LI Xi
%A LIU Guo-sheng
%A <br>王娜
%A 李霞
%A 刘国胜
%J 计算机应用研究
%D 2012
%I 
%X 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.
%K positive constraints
%K feature subspace
%K locality preserving
%K manifold learning<br>正约束
%K 特征子空间
%K 局部保持
%K 流形学习
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=CF88F5976DB5FA5B09FCA6C19FC497EE&yid=99E9153A83D4CB11&vid=771469D9D58C34FF&iid=E158A972A605785F&sid=0E223208672FF713&eid=4882B246E68CA8CB&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=10