%0 Journal Article
%T Neighborhood Margin Fisher Discriminant Analysis<br>近邻边界Fisher判别分析
%A Wei Lai Wang Shou-jue Xu Fei-fei Wang Rui-zhi
%A <br>魏莱
%A 王守觉
%A 徐菲菲
%A 王睿智
%J 电子与信息学报
%D 2009
%I 
%X The curse of high dimensionality is usually a major cause of limitations of many machine learning algorithms. A novel algorithm called Neighborhood Margin Fisher Discriminant Analysis (NMFDA) is proposed for supervised linear dimensionality reduction. For every point, NMFDA tries to enlarge the margin of the farthest point with the same class label and the nearest point with the different class label. Also the Kernel NMFDA is proposed for nonlinear dimensionality reduction. The contrastive experiments on several benchmark face database show the effectiveness of proposed method.
%K Dimensionality reduction
%K Manifold learning
%K Principal Component Analysis(PCA)
%K Fisher discriminant analysis
%K Face recognition<br>维数约简
%K 流形学习
%K 主成份分析
%K Fisher判别分析
%K 人脸识别
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=1319827C0C74AAE8D654BEA21B7F54D3&jid=EFC0377B03BD8D0EF4BBB548AC5F739A&aid=548F52613C605DBBEADDB62B2F36E9B4&yid=DE12191FBD62783C&vid=4AD960B5AD2D111A&iid=38B194292C032A66&sid=4ECB3941871FD391&eid=F9A6B6F259CE5121&journal_id=1009-5896&journal_name=电子与信息学报&referenced_num=2&reference_num=18