%0 Journal Article
%T Probabilistic Two-dimensional Principal Component Analysis<br>概率二维主分量分析
%A QING Xiang-Yun
%A WANG Xing-Yu
%A <br>卿湘运
%A 王行愚
%J 自动化学报
%D 2008
%I 
%X Two-dimensional principal component analysis(2DPCA)is an approach to feature extraction and dimen- sionality reduction for an image represented straightforward as a matrix.In this paper,a probabilistic model for 2DPCA, called P2DPCA,is proposed.First,the principal components(vectors)are derived through maximum-likelihood estima- tion of parameters in the generative probabilistic model.Then,due to dealing properly with missing data,we present an expectation-maximization(EM)algorithm for estimating the parameters of the model and principal components.The application to cluster face images using mixtures of P2DPCA models shows that P2DPCA model can be a tool for density-estimation of image matrix.Experimental results on face image reconstruction with missing data illustrate the effectiveness of the model and the EM iterative algorithm.
%K Principal component analysis(PCA)
%K two-dimensional principal component analysis(2DPCA)
%K expectationmaximization(EM)algorithm
%K missing data<br>主分量分析
%K 二维主分量分析
%K 期望最大化算法
%K 缺失数据
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=E76622685B64B2AA896A7F777B64EB3A&aid=7C5C1D5F9A9138BFEBEED3BB01A689C0&yid=67289AFF6305E306&vid=339D79302DF62549&iid=38B194292C032A66&sid=A5111BA190517959&eid=3356A7630A93A219&journal_id=0254-4156&journal_name=自动化学报&referenced_num=0&reference_num=19