  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.
