%0 Journal Article
%T Efficient Algorithm to Optimal Set of Uncorrelated Discriminates Vectors<br>不相关最佳鉴别矢量集的有效算法
%A CHEN Fu-bing
%A WANG Wen-sheng
%A XIE Yong-hua
%A YANG Jing-yu
%A <br>陈伏兵
%A 王文胜
%A 谢永华
%A 杨静宇
%J 计算机应用研究
%D 2006
%I 
%X Nowadays there are two kinds of methods for dealing with the problems of small sample size in linear discriminant analysis.One is that the aim of avoiding singularity is arrived by dimension reduction of feature vector of pattern samples before pattern recognition.The other is to develop an algorithm to gain the lower discriminant features.By combining the above two kinds of methods,the problem has been solved that how to gain the optimal set of uncorrelated discriminant vectors for small sample size problem based on the generalized Fisher's linear discriminant criterion.An efficient algorithm has been presented in this paper.
%K Feature Extraction
%K Small Sample Size Problem
%K Generalized Linear Discriminates Analysis
%K Uncorrelated Discriminates Vectors
%K Face Recognition<br>特征抽取
%K 小样本问题
%K 广义线性鉴别分析
%K 不相关鉴别矢量
%K 人脸识别
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=DB900CCB692664FB&yid=37904DC365DD7266&vid=EA389574707BDED3&iid=B31275AF3241DB2D&sid=4AD960B5AD2D111A&eid=27746BCEEE58E9DC&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=13