%0 Journal Article
%T A Convergent Algorithm for PCA Neural Network<br>一种全局收敛的PCA神经网络学习算法
%A HUANG Ke-Jun YE Mao WANG Yan-Dong LI Yi-Chao
%A <br>黄克军
%A 叶茂
%A 王雁东
%A 李毅超
%J 计算机科学
%D 2004
%I 
%X Principal component analysis (PCA)is one of the most general-purpose feature extraction methods. For processing the huge data sets, a variety of learning algorithm for PCA has been proposed. However, traditional algorithms will either divergence or convergence very slowly. Based on the CRLS neural network,a novel convergence algorithm is proposed and the fact that the weight vector will converge to the largest eigenvector is also proved. Finally ,simulation results are also included to illustrate the accuracy of this new algorithm.
%K Principal component analysis
%K Neural network
%K Eigenvector
%K Feature extraction<br>全局收敛
%K PCA
%K 神经网络
%K 主元分析
%K K-L变换
%K 学习算法
%K 特征向量
%K 特征提取
%K 模式识别
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=C111A57B8FA3E745&yid=D0E58B75BFD8E51C&vid=4AD960B5AD2D111A&iid=94C357A881DFC066&sid=D59111839E7C8BDF&eid=7CE3F1F20DE6B307&journal_id=1002-137X&journal_name=计算机科学&referenced_num=3&reference_num=12