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On Fractionally-Supervised Classification: Weight Selection and Extension to the Multivariate t-Distribution

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  • Michael P. B. Gallaugher

    (McMaster University)

  • Paul D. McNicholas

    (McMaster University)

Abstract

Recent work on fractionally-supervised classification (FSC), an approach that allows classification to be carried out with a fractional amount of weight given to the unlabelled points, is further developed in two respects. The primary development addresses a question of fundamental importance over how to choose the amount of weight given to the unlabelled points. The resolution of this matter is essential because it makes FSC more readily applicable to real problems. Interestingly, the resolution of the weight selection problem opens up the possibility of a different approach to model selection in model-based clustering and classification. A secondary development demonstrates that the FSC approach can be effective beyond Gaussian mixture models. To this end, an FSC approach is illustrated using mixtures of multivariate t-distributions.

Suggested Citation

  • Michael P. B. Gallaugher & Paul D. McNicholas, 2019. "On Fractionally-Supervised Classification: Weight Selection and Extension to the Multivariate t-Distribution," Journal of Classification, Springer;The Classification Society, vol. 36(2), pages 232-265, July.
    • Handle: RePEc:spr:jclass:v:36:y:2019:i:2:d:10.1007_s00357-018-9280-z
    DOI: 10.1007/s00357-018-9280-z
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    References listed on IDEAS

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    Cited by:

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    2. Utkarsh J. Dang & Michael P.B. Gallaugher & Ryan P. Browne & Paul D. McNicholas, 2023. "Model-Based Clustering and Classification Using Mixtures of Multivariate Skewed Power Exponential Distributions," Journal of Classification, Springer;The Classification Society, vol. 40(1), pages 145-167, April.
    3. Ahfock, Daniel & McLachlan, Geoffrey J., 2023. "Semi-Supervised Learning of Classifiers from a Statistical Perspective: A Brief Review," Econometrics and Statistics, Elsevier, vol. 26(C), pages 124-138.

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