{"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/semi-supervised-nmf-models-for-topic-modeling","title":"Semi-supervised NMF Models for Topic Modeling in Learning Tasks","arxiv_id":"2010.07956","date":"2020-10-15","proceeding":null,"authors":["Jamie Haddock","Lara Kassab","Sixian Li","Alona Kryshchenko","Rachel Grotheer","Elena Sizikova","Chuntian Wang","Thomas Merkh","R. W. M. A. Madushani","Miju Ahn","Deanna Needell","Kathryn Leonard"],"abstract":"We propose several new models for semi-supervised nonnegative matrix factorization (SSNMF) and provide motivation for SSNMF models as maximum likelihood estimators given specific distributions of uncertainty. We present multiplicative updates training methods for each new model, and demonstrate the application of these models to classification, although they are flexible to other supervised learning tasks. We illustrate the promise of these models and training methods on both synthetic and real data, and achieve high classification accuracy on the 20 Newsgroups dataset.","url_abs":"https://arxiv.org/abs/2010.07956v1","url_pdf":"https://arxiv.org/pdf/2010.07956v1.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":"semi-supervised-nmf-models-for-topic-modeling","repo_url":"https://github.com/jamiehadd/ssnmf","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"text-classification","task_name":"Text Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/text-classification-on-20news","task":"Text Classification","dataset":"20NEWS","model":"SSNMF","rank_in_archive_order":13,"of":16,"metrics":{"Accuracy":"81.88"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}