子空间聚类的非参数模型及变分贝叶斯学习.pdf

30 8 Vol. 30 No. 8 2007 8 CHINESE JOURNA L OF COMPU TERS Aug . 2007 卿湘运 王行愚 ( 200237) . , . H off , . Dirichlet , . . , . . UCI , . ; Dirichlet ; ; ; TP 181 Nonparametric Model and Variational Bayesian Learning for Subspace Clustering QING XiangYun WANG XingYu (College of Inf ormation Science and Technology, East China University of Science and Technology, Shanghai 200237) Abstract T he goal of subspace clustering is to group a given set of data represented by different feature subsets. As an unsupervised learning method, subspace clustering tries to discover the patterns of "similarity examined under different presentations" and has received a great deal of in terest and research in the related domains. Firstly the "mean and variance shift" model proposed by Hoff is extended to a new nonparametric model of subspace clustering based on subsets of fea tures. T he advantage of the model is that variational Bayesian method can be applied. The model based on the integration of a Dirichlet process mixture model and a nonparametric model of selec ting subsets of features can automatically choose the number of clusters and perform subspace clustering . Then posterior inference of the model is done using Markov Chain Monte Carlo. Due to computational considerations the authors propose a variational Bayesian method to learn the pa rameters of the model. Experimental results using simulated data and the application to the prob lem of clustering face imag