Title: Model based-clustering via mixtures of two new matrix-variate distributions
Authors: Salvatore Daniele Tomarchio - University of Catania (Italy) [presenting]
Antonio Punzo - University of Catania (Italy)
Luca Bagnato - Catholic University of the Sacred Heart (Italy)
Abstract: Two matrix-variate distributions, both elliptical heavy-tailed generalization of the matrix-variate normal distribution, are introduced. For the nested matrix-variate normal distribution, their probability density functions are characterized by only one additional parameter that governs the tail-weight. Both distributions are then used for model-based clustering via finite mixture models. Being able to handle data with atypical observations in a better way than the matrix-variate normal mixture, the proposed models can avoid the disruption of the true underlying group structure. Several EM-based algorithms are implemented for parameter estimation and tested in terms of computational times and parameter recovery. Furthermore, these mixture models are fitted to simulated and real data, and their fitting and clustering performances are analyzed and compared to those obtained by other well-established competitors.