{"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/scalable-methods-for-nonnegative-matrix","title":"Scalable methods for nonnegative matrix factorizations of near-separable tall-and-skinny matrices","arxiv_id":"1402.6964","date":"2014-02-27","proceeding":"NeurIPS 2014 12","authors":["Austin R. Benson","Jason D. Lee","Bartek Rajwa","David F. Gleich"],"abstract":"Numerous algorithms are used for nonnegative matrix factorization under the\nassumption that the matrix is nearly separable. In this paper, we show how to\nmake these algorithms efficient for data matrices that have many more rows than\ncolumns, so-called \"tall-and-skinny matrices\". One key component to these\nimproved methods is an orthogonal matrix transformation that preserves the\nseparability of the NMF problem. Our final methods need a single pass over the\ndata matrix and are suitable for streaming, multi-core, and MapReduce\narchitectures. We demonstrate the efficacy of these algorithms on\nterabyte-sized synthetic matrices and real-world matrices from scientific\ncomputing and bioinformatics.","url_abs":"http://arxiv.org/abs/1402.6964v1","url_pdf":"http://arxiv.org/pdf/1402.6964v1.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":"scalable-methods-for-nonnegative-matrix","repo_url":"https://github.com/arbenson/mrnmf","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}