title: Mixtures of factor analyzers with common factor loadings: applications to the clustering and visualization of high-dimensional data.
abstract: Mixtures of factor analyzers enable model-based density estimation to be undertaken for high-dimensional data, where the number of observations n is not very large relative to their dimension p. In practice, there is often the need to further reduce the number of parameters in the specification of the component-covariance matrices. To this end, we propose the use of common component-factor loadings, which considerably reduces further the number of parameters. Moreover, it allows the data to be displayed in low--dimensional plots.
authors.affiliation: Department of Statistics, Chonnam National University, Gwangju 500-757, South Korea. jbaek@chonnam.ac.kr
authors.forename: Jangsun
authors.initials: J
authors.lastname: Baek
authors.forename: Geoffrey J
authors.initials: GJ
authors.lastname: McLachlan
authors.forename: Lloyd K
authors.initials: LK
authors.lastname: Flack