{"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/comparative-study-of-inference-methods-for","title":"Comparative Study of Inference Methods for Bayesian Nonnegative Matrix Factorisation","arxiv_id":"1707.05147","date":"2017-07-13","proceeding":null,"authors":["Thomas Brouwer","Jes Frellsen","Pietro Lió"],"abstract":"In this paper, we study the trade-offs of different inference approaches for\nBayesian matrix factorisation methods, which are commonly used for predicting\nmissing values, and for finding patterns in the data. In particular, we\nconsider Bayesian nonnegative variants of matrix factorisation and\ntri-factorisation, and compare non-probabilistic inference, Gibbs sampling,\nvariational Bayesian inference, and a maximum-a-posteriori approach. The\nvariational approach is new for the Bayesian nonnegative models. We compare\ntheir convergence, and robustness to noise and sparsity of the data, on both\nsynthetic and real-world datasets. Furthermore, we extend the models with the\nBayesian automatic relevance determination prior, allowing the models to\nperform automatic model selection, and demonstrate its efficiency.","url_abs":"http://arxiv.org/abs/1707.05147v1","url_pdf":"http://arxiv.org/pdf/1707.05147v1.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":"comparative-study-of-inference-methods-for","repo_url":"https://github.com/ThomasBrouwer/BNMTF_ARD","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"},{"task_slug":"missing-values","task_name":"Missing Values"},{"task_slug":"model-selection","task_name":"Model Selection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}