Model-based clustering and classification via patterned covariance analysis

This work deals with the classification problem in the case that groups are known and both labeled and unlabeled data are available. The classification rule is derived using Gaussian mixtures, with covariance matrices fixed according to a multiple testing procedure, which allows to choose among four alternatives: het- eroscedasticity, homometroscedasticity, homotroposcedasticity, and homoscedastic- ity. The mixture models are then fitted using all available data (labeled and unla- beled) and adopting the EM and the CEM algorithms. Applications on real data are provided in order to show the classification performance of the proposed procedure