# An Expectation-Maximization Algorithm for the Matrix Normal Distribution

arXiv: https://arxiv.org/abs/1309.6609 · HTML: https://mlsanity.com/paper/1309.6609/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2013.

## Built on by (most cited first, 1 of 1)

- [Multivariate Functional Linear Discriminant Analysis for the Classification of Short Time Series with Missing Data](https://mlsanity.com/paper/2402.13103/) (2024)

## Abstract (arXiv)

Dramatic increases in the size and dimensionality of many recent data sets make crucial the need for sophisticated methods that can exploit inherent structure and handle missing values. In this article we derive an expectation-maximization (EM) algorithm for the matrix normal distribution, a distribution well-suited for naturally structured data such as spatio-temporal data. We review previously established maximum likelihood matrix normal estimates, and then consider the situation involving missing data. We apply our EM method in a simulation study exploring errors across different dimensions and proportions of missing data. We compare these errors and computational running times to those from two alternative methods. Finally, we implement the proposed EM method on a satellite image dataset to investigate land-cover classification separability.