# Parsimonious Mixtures of Matrix Variate Bilinear Factor Analyzers

arXiv: https://arxiv.org/abs/1911.09012 · HTML: https://mlsanity.com/paper/1911.09012/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2019.

## 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)

Over the years, data have become increasingly higher dimensional, which has prompted an increased need for dimension reduction techniques. This is perhaps especially true for clustering (unsupervised classification) as well as semi-supervised and supervised classification. Many methods have been proposed in the literature for two-way (multivariate) data and quite recently methods have been presented for three-way (matrix variate) data. One such such method is the mixtures of matrix variate bilinear factor analyzers (MMVBFA) model. Herein, we propose of total of 64 parsimonious MMVBFA models. Simulated and real data are used for illustration.