Campus Units

Aerospace Engineering, Materials Science and Engineering, Mechanical Engineering, Statistics

Document Type

Article

Publication Version

Submitted Manuscript

Publication Date

2019

Journal or Book Title

arXiv

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.

Comments

This is a pre-print of the article Thompson, Geoffrey Z., Ranjan Maitra, William Q. Meeker, and Ashraf Bastawros. "Classification with the matrix-variate-t distribution." arXiv preprint arXiv:1907.09565 (2019). Posted with permission.

Copyright Owner

The Authors

Language

en

File Format

application/pdf

Published Version

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