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Factor and hybrid components for model-based clustering

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  • Jason Hou-Liu

    (University of Waterloo)

  • Ryan P. Browne

    (University of Waterloo)

Abstract

A major challenge when performing model-based clustering is a large increase in the number of free parameters as the data dimensionality increases. To combat this issue, parsimonious methods such allow component covariance matrices to share parameters by exploiting geometric redundancies. The present work considers an additional level of intracluster structure that also captures hybridisation of mean and covariance parameters between components for the multivariate normal distribution. We posit components with heterogeneous parameterisation; a subset are considered factor components and have explicit mean and covariance parameters, and the remainder are considered hybrid components that have means and covariances implied by a set of factor loadings that weight factor component parameters. An estimation procedure is provided using the Expectation-Maximization algorithm, and comparison to Gaussian mixture models with parsimonious covariances is made by evaluation on a collection of datasets.

Suggested Citation

  • Jason Hou-Liu & Ryan P. Browne, 2022. "Factor and hybrid components for model-based clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(2), pages 373-398, June.
    • Handle: RePEc:spr:advdac:v:16:y:2022:i:2:d:10.1007_s11634-021-00483-2
    DOI: 10.1007/s11634-021-00483-2
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    References listed on IDEAS

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