{"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/fast-bayesian-non-negative-matrix","title":"Fast Bayesian Non-Negative Matrix Factorisation and Tri-Factorisation","arxiv_id":"1610.08127","date":"2016-10-26","proceeding":null,"authors":["Thomas Brouwer","Jes Frellsen","Pietro Lio'"],"abstract":"We present a fast variational Bayesian algorithm for performing non-negative\nmatrix factorisation and tri-factorisation. We show that our approach achieves\nfaster convergence per iteration and timestep (wall-clock) than Gibbs sampling\nand non-probabilistic approaches, and do not require additional samples to\nestimate the posterior. We show that in particular for matrix tri-factorisation\nconvergence is difficult, but our variational Bayesian approach offers a fast\nsolution, allowing the tri-factorisation approach to be used more effectively.","url_abs":"http://arxiv.org/abs/1610.08127v1","url_pdf":"http://arxiv.org/pdf/1610.08127v1.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":"fast-bayesian-non-negative-matrix","repo_url":"https://github.com/ThomasBrouwer/HMF","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}