%0 Journal Article
%T Learning Algorithm of Center Based Neighborhood Embedding for Face Recognition<br>中心近邻嵌入学习算法的人脸识别研究
%A KONG Wan-zeng
%A ZHU Shan-an
%A <br>孔万增
%A 朱善安
%J 中国图象图形学报
%D 2008
%I 
%X In this paper,a novel learning algorithm called center based neighborhood embedding(CNE) is proposed to deal with face recognition. Unlike the classical methods such as local linear embedding(LLE) and local preserving projection(LPP),CNE is a supervised linear dimensionality reduction method. It first computes centers of all sample classes. The input of the weight function between two samples was replaced by center based neighborhood(CN) distance. Then,the high-dimensional data are embedded into a low-dimensional space with preserving the CN geometric structure. The CNE approach is compared with principle component analysis(PCA),linear discriminant analysis(LDA) and local preserving projection(LPP) on ORL,Yale and UMIST databases. Experiments demonstrate the proposed method is superior to other three methods in terms of both lower-dimensional visualization and recognition accuracy.
%K face recognition
%K center based neighborhood embedding
%K supervised learning
%K linear dimensionality reduction<br>人脸识别
%K 中心近邻嵌入
%K 有监督学习
%K 线性降维
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=DE7BB0CCC6107B7B9B6F4585A877610D&yid=67289AFF6305E306&vid=FC0714F8D2EB605D&iid=E158A972A605785F&sid=09F7C8D609E885AE&eid=08B2E838F29A693A&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=1&reference_num=11