CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2010 vol.32 Issue No.07 - July

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Issue No.07 - July (2010 vol.32)

pp: 1298-1309

Geoffrey J. McLachlan , University of Queensland, Brisbane

Jangsun Baek , Chonnam National University, Gwangju

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.

INDEX TERMS

Normal mixture models, mixtures of factor analyzers, common factor loadings, model-based clustering.

CITATION

Geoffrey J. McLachlan, Jangsun Baek, "Mixtures of Factor Analyzers with Common Factor Loadings: Applications to the Clustering and Visualization of High-Dimensional Data",

*IEEE Transactions on Pattern Analysis & Machine Intelligence*, vol.32, no. 7, pp. 1298-1309, July 2010, doi:10.1109/TPAMI.2009.149REFERENCES