{"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/fast-l1-nmf-for-multiple-parametric-model","title":"Fast L1-NMF for Multiple Parametric Model Estimation","arxiv_id":"1610.05712","date":"2016-10-18","proceeding":null,"authors":["Mariano Tepper","Guillermo Sapiro"],"abstract":"In this work we introduce a comprehensive algorithmic pipeline for multiple\nparametric model estimation. The proposed approach analyzes the information\nproduced by a random sampling algorithm (e.g., RANSAC) from a machine\nlearning/optimization perspective, using a \\textit{parameterless} biclustering\nalgorithm based on L1 nonnegative matrix factorization (L1-NMF). The proposed\nframework exploits consistent patterns that naturally arise during the RANSAC\nexecution, while explicitly avoiding spurious inconsistencies. Contrarily to\nthe main trends in the literature, the proposed technique does not impose\nnon-intersecting parametric models. A new accelerated algorithm to compute\nL1-NMFs allows to handle medium-sized problems faster while also extending the\nusability of the algorithm to much larger datasets. This accelerated algorithm\nhas applications in any other context where an L1-NMF is needed, beyond the\nbiclustering approach to parameter estimation here addressed. We accompany the\nalgorithmic presentation with theoretical foundations and numerous and diverse\nexamples.","url_abs":"http://arxiv.org/abs/1610.05712v2","url_pdf":"http://arxiv.org/pdf/1610.05712v2.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":"fast-l1-nmf-for-multiple-parametric-model","repo_url":"https://github.com/marianotepper/arse","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"model","task_name":"model"},{"task_slug":"parameter-estimation","task_name":"parameter estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}