Mixtures of factor analyzers with common factor loadings: applications to the clustering and visualization of high-dimensional data.
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.
Baek
Jangsun
J
Department of Statistics, Chonnam National University, Gwangju 500-757, South Korea. jbaek@chonnam.ac.kr
McLachlan
Geoffrey J
GJ
Flack
Lloyd K
LK