Home Page

Papers

Submissions

News

Editorial Board

Proceedings

Open Source Software

Search

Statistics

Login

Frequently Asked Questions

Contact Us



RSS Feed

Penalized Model-Based Clustering with Application to Variable Selection

Wei Pan, Xiaotong Shen; 8(41):1145−1164, 2007.

Abstract

Variable selection in clustering analysis is both challenging and important. In the context of model-based clustering analysis with a common diagonal covariance matrix, which is especially suitable for "high dimension, low sample size" settings, we propose a penalized likelihood approach with an L1 penalty function, automatically realizing variable selection via thresholding and delivering a sparse solution. We derive an EM algorithm to fit our proposed model, and propose a modified BIC as a model selection criterion to choose the number of components and the penalization parameter. A simulation study and an application to gene function prediction with gene expression profiles demonstrate the utility of our method.

[abs][pdf][bib]       
© JMLR 2007. (edit, beta)