Online Program Home
My Program

Abstract Details

Activity Number: 46 - Recent Advances in Cluster Analysis and Cluster Validation
Type: Invited
Date/Time: Sunday, July 29, 2018 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #326844
Title: A Mixture of Matrix Variate Bilinear Factor Analyzers
Author(s): Paul McNicholas*
Companies: McMaster University
Keywords: Clustering; factor analysis; matrix variate; mixture models

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.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2018 program