# Classification with the matrix-variate-$t$ distribution

## TL;DR

This paper introduces an EM algorithm for classification using matrix-variate-$t$ distributions, effectively modeling dependencies in matrix-valued data for applications like imaging and forensic analysis.

## Contribution

It develops a novel EM-based discriminant analysis method specifically for matrix-variate-$t$ distributions, addressing dependence structures in complex data.

## Key findings

- Effective on simulated datasets
- Useful for forensic surface matching
- Applicable to various imaging modalities

## 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.