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Mixtures of Factor Analyzers with Common Factor Loadings: Applications to the Clustering and Visualization of High-Dimensional Data

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3 Author(s)
Jangsun Baek ; Chonnam National University, Gwangju ; Geoffrey J. McLachlan ; Lloyd K. Flack

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.

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:32 ,  Issue: 7 )