  This paper exploits a simplified version of the mixture of multivariate
t-factor analyzers (MtFA) for robust mixture modelling and clustering of
high-dimensional data that frequently contain a number of outliers. Two classes
of eight parsimonious t mixture models are introduced and computation of
maximum likelihood estimates of parameters is achieved using the alternating
expectation conditional maximization (AECM) algorithm. The usefulness of the
methodology is illustrated through applications of image compression and
compact facial representation.
