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

TL;DR

This paper advances fractionally-supervised classification by addressing weight selection for unlabelled data and extends its application from Gaussian to multivariate t-distribution models, enhancing its practical utility.

Contribution

It introduces a method for selecting the optimal weight for unlabelled data in FSC and demonstrates FSC's effectiveness with multivariate t-distribution models.

Findings

Effective weight selection method for FSC.

FSC with t-distributions improves robustness.

Enhanced applicability of FSC beyond Gaussian models.

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.