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Functional data clustering by projection into latent generalized hyperbolic subspaces

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  • Alex Sharp

    (University of Waterloo)

  • Ryan Browne

    (University of Waterloo)

Abstract

We introduce a latent subpace model which facilitates model-based clustering of functional data. Flexible clustering is attained by imposing jointly generalized hyperbolic distributions on projections of basis expansion coefficients into group specific subspaces. The model acquires parsimony by assuming these subspaces are of relatively low dimension. Parameter estimation is done through a multicycle ECM algorithm. Application to simulated and real datasets illustrate competitive clustering capabilities, and demonstrate the models general applicability.

Suggested Citation

  • Alex Sharp & Ryan Browne, 2021. "Functional data clustering by projection into latent generalized hyperbolic subspaces," 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. 15(3), pages 735-757, September.
    • Handle: RePEc:spr:advdac:v:15:y:2021:i:3:d:10.1007_s11634-020-00432-5
    DOI: 10.1007/s11634-020-00432-5
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    References listed on IDEAS

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    3. McLachlan, G. J. & Peel, D. & Bean, R. W., 2003. "Modelling high-dimensional data by mixtures of factor analyzers," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 379-388, January.
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    6. Charles Bouveyron & Julien Jacques, 2011. "Model-based clustering of time series in group-specific functional subspaces," 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. 5(4), pages 281-300, December.
    7. McLachlan, G.J. & Bean, R.W. & Ben-Tovim Jones, L., 2007. "Extension of the mixture of factor analyzers model to incorporate the multivariate t-distribution," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5327-5338, July.
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    Cited by:

    1. Christian Acal & Ana M. Aguilera, 2023. "Basis expansion approaches for functional analysis of variance with repeated measures," 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. 17(2), pages 291-321, June.

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