Benhuai Xie1, Wei Pan, Xiaotong Shen. 1. Division of Biostatistics, School of Public Health and School of Statistics, University of Minnesota, Minneapolis, MN, USA.
Abstract
MOTIVATION: Model-based clustering has been widely used, e.g. in microarray data analysis. Since for high-dimensional data variable selection is necessary, several penalized model-based clustering methods have been proposed tørealize simultaneous variable selection and clustering. However, the existing methods all assume that the variables are independent with the use of diagonal covariance matrices. RESULTS: To model non-independence of variables (e.g. correlated gene expressions) while alleviating the problem with the large number of unknown parameters associated with a general non-diagonal covariance matrix, we generalize the mixture of factor analyzers to that with penalization, which, among others, can effectively realize variable selection. We use simulated data and real microarray data to illustrate the utility and advantages of the proposed method over several existing ones.
MOTIVATION: Model-based clustering has been widely used, e.g. in microarray data analysis. Since for high-dimensional data variable selection is necessary, several penalized model-based clustering methods have been proposed tørealize simultaneous variable selection and clustering. However, the existing methods all assume that the variables are independent with the use of diagonal covariance matrices. RESULTS: To model non-independence of variables (e.g. correlated gene expressions) while alleviating the problem with the large number of unknown parameters associated with a general non-diagonal covariance matrix, we generalize the mixture of factor analyzers to that with penalization, which, among others, can effectively realize variable selection. We use simulated data and real microarray data to illustrate the utility and advantages of the proposed method over several existing ones.
Authors: David G Beer; Sharon L R Kardia; Chiang-Ching Huang; Thomas J Giordano; Albert M Levin; David E Misek; Lin Lin; Guoan Chen; Tarek G Gharib; Dafydd G Thomas; Michelle L Lizyness; Rork Kuick; Satoru Hayasaka; Jeremy M G Taylor; Mark D Iannettoni; Mark B Orringer; Samir Hanash Journal: Nat Med Date: 2002-07-15 Impact factor: 53.440