Over the years data has 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. Although dimension reduction in the area of clustering for multivariate data has been quite thoroughly discussed in the literature, there is relatively little work in the area of three way, or matrix variate, data. This talk will give a background in clustering matrix variate data, specifically using mixtures of skewed matrix variate distributions, followed by a discussion of the mixture of matrix variate bilinear factor analyzers (MMVBFA) model used for dimension reduction in both rows and columns of higher dimensional matrix variate data. Simulated data as well as real image data will be used for illustration.