A Mixture of Matrix Variate Bilinear Factor Analyzers
This work addresses a gap in dimension reduction techniques for three-way data clustering, which is an incremental advancement in a domain-specific area.
The authors tackled the problem of clustering high-dimensional matrix variate data by developing a mixture of matrix variate bilinear factor analyzers (MMVBFA) model, which they claim is the first of its kind, and demonstrated its application on simulated and real data.
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 within the literature, there is relatively little work in the area of three-way, or 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. This work can be considered both the first matrix variate bilinear factor analysis model as well as the first MMVBFA model. Parameter estimation is discussed, and the MMVBFA model is illustrated using simulated and real data.