{"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/collapsed-variational-inference-for","title":"Collapsed Variational Inference for Nonparametric Bayesian Group Factor Analysis","arxiv_id":"1809.03566","date":"2018-09-10","proceeding":null,"authors":["Sikun Yang","Heinz Koeppl"],"abstract":"Group factor analysis (GFA) methods have been widely used to infer the common\nstructure and the group-specific signals from multiple related datasets in\nvarious fields including systems biology and neuroimaging. To date, most\navailable GFA models require Gibbs sampling or slice sampling to perform\ninference, which prevents the practical application of GFA to large-scale data.\nIn this paper we present an efficient collapsed variational inference (CVI)\nalgorithm for the nonparametric Bayesian group factor analysis (NGFA) model\nbuilt upon an hierarchical beta Bernoulli process. Our CVI algorithm proceeds\nby marginalizing out the group-specific beta process parameters, and then\napproximating the true posterior in the collapsed space using mean field\nmethods. Experimental results on both synthetic and real-world data demonstrate\nthe effectiveness of our CVI algorithm for the NGFA compared with\nstate-of-the-art GFA methods.","url_abs":"http://arxiv.org/abs/1809.03566v2","url_pdf":"http://arxiv.org/pdf/1809.03566v2.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":"collapsed-variational-inference-for","repo_url":"https://github.com/stephenyang/CVB_NGFA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}