{"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/efficient-learning-of-mixed-membership-models","title":"Efficient Learning of Mixed Membership Models","arxiv_id":"1702.07933","date":"2017-02-25","proceeding":null,"authors":["Zilong Tan","Sayan Mukherjee"],"abstract":"We present an efficient algorithm for learning mixed membership models when\nthe number of variables $p$ is much larger than the number of hidden components\n$k$. This algorithm reduces the computational complexity of state-of-the-art\ntensor methods, which require decomposing an $O\\left(p^3\\right)$ tensor, to\nfactorizing $O\\left(p/k\\right)$ sub-tensors each of size $O\\left(k^3\\right)$.\nIn addition, we address the issue of negative entries in the empirical method\nof moments based estimators. We provide sufficient conditions under which our\napproach has provable guarantees. Our approach obtains competitive empirical\nresults on both simulated and real data.","url_abs":"http://arxiv.org/abs/1702.07933v3","url_pdf":"http://arxiv.org/pdf/1702.07933v3.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":"efficient-learning-of-mixed-membership-models","repo_url":"https://github.com/ZilongTan/ptpqp","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"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}