Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
415627 | Computational Statistics & Data Analysis | 2007 | 12 Pages |
Abstract
Mixtures of factor analyzers enable model-based density estimation to be undertaken for high-dimensional data, where the number of observations n is small relative to their dimension p. However, this approach is sensitive to outliers as it is based on a mixture model in which the multivariate normal family of distributions is assumed for the component error and factor distributions. An extension to mixtures of t-factor analyzers is considered, whereby the multivariate t-family is adopted for the component error and factor distributions. An EM-based algorithm is developed for the fitting of mixtures of t-factor analyzers. Its application is demonstrated in the clustering of some microarray gene-expression data.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
G.J. McLachlan, R.W. Bean, L. Ben-Tovim Jones,