A mixture of common skew-t factor analyzers model is introduced for
model-based clustering of high-dimensional data. By assuming common component
factor loadings, this model allows clustering to be performed in the presence
of a large number of mixture components or when the number of dimensions is too
large to be well-modelled by the mixtures of factor analyzers model or a
variant thereof. Furthermore, assuming that the component densities follow a
skew-t distribution allows robust clustering of skewed data. The alternating
expectation-conditional maximization algorithm is employed for parameter
estimation. We demonstrate excellent clustering performance when our model is
applied to real and simulated data.This paper marks the first time that skewed
common factors have been used.