Stochastic model selection for Mixtures of Matrix-Normals
Methodology
2013-03-07 v3 Computation
Abstract
Finite mixtures of matrix normal distributions are a powerful tool for classifying three-way data in unsupervised problems. The distribution of each component is assumed to be a matrix variate normal density. The mixture model can be estimated through the EM algorithm under the assumption that the number of components is known and fixed. In this work we introduce, develop and explore a Bayesian analysis of the model in order to provide a tool for simultaneous model estimation and model selection. The effectiveness of the proposed method is illustrated on a simulation study and on a real example.
Cite
@article{arxiv.1010.2310,
title = {Stochastic model selection for Mixtures of Matrix-Normals},
author = {Cinzia Viroli},
journal= {arXiv preprint arXiv:1010.2310},
year = {2013}
}
Comments
This paper has been withdrawn by the author. Some content of the work paper has been extended