Mixtures of Factor Analyzers with Common Factor Loadings: Applications to the Clustering and Visualization of High-Dimensional Data
Issue No. 07 - July (2010 vol. 32)
Jangsun Baek , Chonnam National University, Gwangju
Geoffrey J. McLachlan , University of Queensland, Brisbane
Lloyd K. Flack , University of Queensland, Brisbane
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.
Normal mixture models, mixtures of factor analyzers, common factor loadings, model-based clustering.
G. J. McLachlan, L. K. Flack and J. Baek, "Mixtures of Factor Analyzers with Common Factor Loadings: Applications to the Clustering and Visualization of High-Dimensional Data," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 32, no. , pp. 1298-1309, 2009.