title: Penalized mixtures of factor analyzers with application to clustering high-dimensional microarray data.
abstract: BACKGROUND
  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.affiliation: Division of Biostatistics, School of Public Health and School of Statistics, University of Minnesota, Minneapolis, MN, USA.
authors.forename: Benhuai
authors.initials: B
authors.lastname: Xie
authors.forename: Wei
authors.initials: W
authors.lastname: Pan
authors.forename: Xiaotong
authors.initials: X
authors.lastname: Shen
mesh_terms: D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*