Mixtures of Matrix Variate Bilinear Factor Analyzers

12/22/2017

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by   Michael P. B. Gallaugher, et al.

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Over the years data is becoming increasingly higher dimensional, which has prompted an increased need for dimension reduction techniques, in particular for clustering and classification. Although dimension reduction in the area of clustering for multivariate data has been thoroughly discussed in the literature there is relatively little work in the area of three way (matrix variate) data. Herein, we develop a mixture of matrix variate bilinear factor analyzers (MMVBFA) model for use in clustering high dimensional matrix variate data. Parameter estimation is discussed, and the MMVBFA model is illustrated using simulated data.