Mixtures of factor analyzers with common factor loadings: applications to the clustering and visualization of high-dimensional data.
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- Record sourced from PubMed, PMID 20489231.
- Also identified by DOI 10.1109/TPAMI.2009.149.
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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.