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Classification with the matrix-variate-t distribution.

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 t-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.

SUBMITTER: Thompson GZ

PROVIDER: S-EPMC7954198 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Classification with the matrix-variate-t distribution.

Thompson Geoffrey Z GZ Maitra Ranjan R Meeker William Q WQ Bastawros Ashraf F AF

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America 20200122 3

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 t-distributions. The methodology shows promise on simulated datasets or when applied to the forensic matching of fractured surfaces or the classification of functional ...[more]

PMID: 33716477

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