Parsimonious Mixtures of Matrix Variate Bilinear Factor Analyzers

TL;DR

This paper introduces 64 parsimonious mixtures of matrix variate bilinear factor analyzers (MMVBFA) models to improve high-dimensional data clustering, demonstrating their effectiveness through simulations and real data applications.

Contribution

The paper proposes a comprehensive set of 64 parsimonious MMVBFA models for better dimension reduction and clustering of matrix variate data, expanding existing methodologies.

Findings

Successful application to simulated data

Effective clustering on real datasets

Demonstrated model flexibility and efficiency

Abstract

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.