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.
Keywords
Cluster analysisFactor (programming language)Component (thermodynamics)Dimension (graph theory)VisualizationComputer scienceFactor analysisData miningCovariancePrincipal component analysisCovariance matrixData visualizationData modelingPattern recognition (psychology)Artificial intelligenceAlgorithmStatisticsMathematicsMachine learningDatabase
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Publication Info
- Year
- 2009
- Type
- article
- Volume
- 32
- Issue
- 7
- Pages
- 1298-1309
- Citations
- 159
- Access
- Closed
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Cite This
Jangsun Baek,
Geoffrey J. McLachlan,
L.K. Flack
(2009).
Mixtures of Factor Analyzers with Common Factor Loadings: Applications to the Clustering and Visualization of High-Dimensional Data.
IEEE Transactions on Pattern Analysis and Machine Intelligence
, 32
(7)
, 1298-1309.
https://doi.org/10.1109/tpami.2009.149
Identifiers
- DOI
- 10.1109/tpami.2009.149