{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-mixture-of-matrix-variate-bilinear-factor","title":"A Mixture of Matrix Variate Bilinear Factor Analyzers","arxiv_id":"1712.08664","date":"2017-12-22","proceeding":null,"authors":["Michael P. B. Gallaugher","Paul D. McNicholas"],"abstract":"Over the years data has become increasingly higher dimensional, which has\nprompted an increased need for dimension reduction techniques. This is perhaps\nespecially true for clustering (unsupervised classification) as well as\nsemi-supervised and supervised classification. Although dimension reduction in\nthe area of clustering for multivariate data has been quite thoroughly\ndiscussed within the literature, there is relatively little work in the area of\nthree-way, or matrix variate, data. Herein, we develop a mixture of matrix\nvariate bilinear factor analyzers (MMVBFA) model for use in clustering\nhigh-dimensional matrix variate data. This work can be considered both the\nfirst matrix variate bilinear factor analysis model as well as the first MMVBFA\nmodel. Parameter estimation is discussed, and the MMVBFA model is illustrated\nusing simulated and real data.","url_abs":"http://arxiv.org/abs/1712.08664v3","url_pdf":"http://arxiv.org/pdf/1712.08664v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-mixture-of-matrix-variate-bilinear-factor","repo_url":"https://github.com/nikpocuca/MatrixVariate.jl","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"dimensionality-reduction","task_name":"Dimensionality Reduction"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"parameter-estimation","task_name":"parameter estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}