{"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/randomized-nonnegative-matrix-factorization","title":"Randomized Nonnegative Matrix Factorization","arxiv_id":"1711.02037","date":"2017-11-06","proceeding":null,"authors":["N. Benjamin Erichson","Ariana Mendible","Sophie Wihlborn","J. Nathan Kutz"],"abstract":"Nonnegative matrix factorization (NMF) is a powerful tool for data mining.\nHowever, the emergence of `big data' has severely challenged our ability to\ncompute this fundamental decomposition using deterministic algorithms. This\npaper presents a randomized hierarchical alternating least squares (HALS)\nalgorithm to compute the NMF. By deriving a smaller matrix from the nonnegative\ninput data, a more efficient nonnegative decomposition can be computed. Our\nalgorithm scales to big data applications while attaining a near-optimal\nfactorization. The proposed algorithm is evaluated using synthetic and real\nworld data and shows substantial speedups compared to deterministic HALS.","url_abs":"http://arxiv.org/abs/1711.02037v2","url_pdf":"http://arxiv.org/pdf/1711.02037v2.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":"randomized-nonnegative-matrix-factorization","repo_url":"https://github.com/Benli11/ristretto","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}},{"paper_slug":"randomized-nonnegative-matrix-factorization","repo_url":"https://github.com/erichson/ristretto","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}}],"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}