{"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/nonnegative-matrix-factorization-nmf-with","title":"Nonnegative Matrix Factorization (NMF) with Heteroscedastic Uncertainties and Missing data","arxiv_id":"1612.06037","date":"2016-12-19","proceeding":null,"authors":["Guangtun Zhu"],"abstract":"Dimensionality reduction and matrix factorization techniques are important and useful machine-learning techniques in many fields. Nonnegative matrix factorization (NMF) is particularly useful for spectral analysis and image processing in astronomy. I present the vectorized update rules and an independent proof of their convergence for NMF with heteroscedastic measurements and missing data. I release a Python implementation of the rules and use an optical spectroscopic dataset of extragalactic sources as an example for demonstration. A future paper will present results of applying the technique to image processing of planetary disks.","url_abs":"http://arxiv.org/abs/1612.06037v1","url_pdf":"http://arxiv.org/pdf/1612.06037v1.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"nonnegative-matrix-factorization-nmf-with","repo_url":"https://github.com/guangtunbenzhu/NonnegMFPy","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"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}