# Classification with the matrix-variate-t distribution

arXiv: https://arxiv.org/abs/1907.09565 · HTML: https://mlsanity.com/paper/1907.09565/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2019.

## Built on by (most cited first, 4 of 4)

- [Robust factored principal component analysis for matrix-valued outlier accommodation and detection](https://mlsanity.com/paper/2112.06760/) (2021)
- [Quantitative Matching of Forensic Evidence Fragments Utilizing 3D Microscopy Analysis of Fracture Surface Replicas](https://mlsanity.com/paper/2109.11972/) (2021)
- [Elliptically-Contoured Tensor-variate Distributions with Application to Improved Image Learning](https://mlsanity.com/paper/2211.06940/) (2022)
- [Nonparametric Linear Discriminant Analysis for High Dimensional Matrix-Valued Data](https://mlsanity.com/paper/2507.19028/) (2025)

## Abstract (arXiv)

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.