Unsupervised component-wise EM learning for finite mixtures of skew t-distributions

In recent years, finite mixtures of skew distributions are gaining popularity as a flexible tool for modelling data with asymmetric distributional features. Parameter estimation for these mixture models via the traditional EM algorithm requires the number of components to be specified a priori. In this paper, we consider unsupervised learning of skew mixture models where the optimal number of components is estimated during the parameter estimation process. We adopt a componentwise EM algorithm and use the minimum message length (MML) criterion. For illustrative purposes, we focus on the case of a finite mixture of multivariate skew t distributions. The performance of the approach is demonstrated on a real dataset from flow cytometry, where our mixture model was used to provide an automated segmentation of cell populations