{"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/dictionary-learning-for-massive-matrix","title":"Dictionary Learning for Massive Matrix Factorization","arxiv_id":"1605.00937","date":"2016-05-03","proceeding":null,"authors":["Arthur Mensch","Julien Mairal","Bertrand Thirion","Gaël Varoquaux"],"abstract":"Sparse matrix factorization is a popular tool to obtain interpretable data\ndecompositions, which are also effective to perform data completion or\ndenoising. Its applicability to large datasets has been addressed with online\nand randomized methods, that reduce the complexity in one of the matrix\ndimension, but not in both of them. In this paper, we tackle very large\nmatrices in both dimensions. We propose a new factoriza-tion method that scales\ngracefully to terabyte-scale datasets, that could not be processed by previous\nalgorithms in a reasonable amount of time. We demonstrate the efficiency of our\napproach on massive functional Magnetic Resonance Imaging (fMRI) data, and on\nmatrix completion problems for recommender systems, where we obtain significant\nspeed-ups compared to state-of-the art coordinate descent methods.","url_abs":"http://arxiv.org/abs/1605.00937v2","url_pdf":"http://arxiv.org/pdf/1605.00937v2.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":"dictionary-learning-for-massive-matrix","repo_url":"https://github.com/arthurmensch/modl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"collaborative-filtering","task_name":"Collaborative Filtering"},{"task_slug":"dictionary-learning","task_name":"Dictionary Learning"},{"task_slug":"matrix-completion","task_name":"Matrix Completion"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/collaborative-filtering-on-movielens-10m","task":"Recommendation Systems","dataset":"MovieLens 10M","model":"Factorization with dictionary learning","rank_in_archive_order":13,"of":17,"metrics":{"RMSE":"0.799"},"uses_additional_data":false},{"leaderboard":"/sota/collaborative-filtering-on-movielens-1m","task":"Recommendation Systems","dataset":"MovieLens 1M","model":"Factorization with dictionary learning","rank_in_archive_order":16,"of":31,"metrics":{"RMSE":"0.866"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}