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Classification with the Matrix-variate- Distribution

Overview
Journal J Comput Graph Stat
Date 2021 Mar 15
PMID 33716477
Citations 4
Authors
Geoffrey Z Thompson
Ranjan Maitra
William Q Meeker
Ashraf F Bastawros
Affiliations
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Abstract

Matrix-variate distributions can intuitively model the dependence structure of matrix-valued observations that arise in applications with multivariate time series, spatio-temporal or repeated measures. This paper develops an Expectation-Maximization algorithm for discriminant analysis and classification with matrix-variate -distributions. The methodology shows promise on simulated datasets or when applied to the forensic matching of fractured surfaces or the classification of functional Magnetic Resonance, satellite or hand gestures images.

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