Bayesian Methods and Mixture Models
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Abstract
Abstract
Mixture models which cluster skewed random matrices can often suffer from over-parameterization in the absence of performing dimension reduction. Even with the use of bilinear factor analyzers, further parameter reduction can be achieved by constraining parameters over clusters. In this manuscript propose a parsimonious family of 256 models for mixtures of skewed matrix variate bilinear factor analyzers, specifically in the case of the skew t distribution. An AECM algorithm for parameter estimation is discussed in detail. Further, extensive simulations are performed, and the method is considered in the case of the MNIST dataset and the Olivetti faces dataset.
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@article{Moore2026Parsimonious,
title = {Parsimonious Mixtures of Skewed Bilinear Factor Analyzers},
author = {Jacob Moore and Michael P. B. Gallaugher},
journal = {arXiv (Cornell University)},
year = {2026},
doi = {10.48550/arxiv.2607.14297},
url = {https://doi.org/10.48550/arxiv.2607.14297}
}FAQ
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