Title: Mixtures of Skewed Matrix Variate Bilinear Factor Analyzers,
Abstract: Clustering is the process of finding and analyzing underlying group structure
in data. In recent years, data as 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
models 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.