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 article 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 to the classification of functional magnetic resonance, satellite, or hand gestures images. Supplementary materials for this article are available online.

Keywords:

• BIC
• ECME
• fMRI
• Fracture mechanics
• LANDSAT
• Supervised learning

Acknowledgments

The authors thank the anonymous reviewer and the associate editor whose detailed comments and suggestions greatly improved the quality of this article.

Additional information

Funding

This research was supported in part by the National Institute of Justice (NIJ) under grant nos. 2015-DN-BX-K056 and 2018-R2-CX-0034. The research of the second author was also supported in part by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) of the National Institutes of Health (NIH) under grant R21EB016212, and the United States Department of Agriculture (USDA) National Institute of Food and Agriculture (NIFA) Hatch project IOW03617. The content of this article is, however, solely the responsibility of the authors and does not represent the official views of the NIJ, the NIBIB, the NIH, the NIFA or the USDA.