# Mixtures of Skewed Matrix Variate Bilinear Factor Analyzers

arXiv: https://arxiv.org/abs/1809.02385 · HTML: https://mlsanity.com/paper/1809.02385/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2018.

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

- [Parsimonious Mixtures of Skewed Bilinear Factor Analyzers](https://mlsanity.com/paper/2607.14297/) (2026)

## Abstract (arXiv)

In recent years, data have become increasingly higher dimensional and, therefore, an increased need has arisen for dimension reduction techniques for clustering. Although such techniques are firmly established in the literature for multivariate data, there is a relative paucity in the area of matrix variate, or three-way, data. Furthermore, the few methods that are available all assume matrix variate normality, which is not always sensible if cluster skewness or excess kurtosis is present. Mixtures of bilinear factor analyzers using skewed matrix variate distributions are proposed. In all, four such mixture models are presented, based on matrix variate skew-t, generalized hyperbolic, variance-gamma, and normal inverse Gaussian distributions, respectively.