%0 Journal Article
%T ADAPTIVE ROBUST PRINCIPAL COMPONENT ANALYSIS BASED ON ERROR MODELING<br>基于误差模型的自适应鲁棒主成分分析
%A WANG Song
%A XIA Shaowei
%A <br>王松
%A 夏绍玮
%J 自动化学报
%D 1999
%I 
%X One way to improve the robustness of principal component analysis (PCA) is studied in the paper. A new adaptive algorithm of robust PCA based on the structure of single layer neural network (NN) is developed with modification of the cost function which can be acquired through modeling of the error function. The new nonlinear robust PCA algorithm can reduce the effects of outliers on the accuracy and convergence of the PCA algorithm through proper processing of them.
%K Principal component analysis (PCA)
%K adaptive robust PCA
%K outliers
%K neural network (NN)
%K maximum likelihood estimate<br>主成分分析
%K 鲁棒性
%K 误差模型
%K 协方差分析
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=E76622685B64B2AA896A7F777B64EB3A&aid=D0058B5ED3AEFFA0&yid=B914830F5B1D1078&vid=C5154311167311FE&iid=E158A972A605785F&sid=652E7E360EBE3082&eid=20ADD38F841C6A4B&journal_id=0254-4156&journal_name=自动化学报&referenced_num=0&reference_num=3