{"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/a-variational-approach-to-stable-principal","title":"A variational approach to stable principal component pursuit","arxiv_id":"1406.1089","date":"2014-06-04","proceeding":null,"authors":["Aleksandr Aravkin","Stephen Becker","Volkan Cevher","Peder Olsen"],"abstract":"We introduce a new convex formulation for stable principal component pursuit\n(SPCP) to decompose noisy signals into low-rank and sparse representations. For\nnumerical solutions of our SPCP formulation, we first develop a convex\nvariational framework and then accelerate it with quasi-Newton methods. We\nshow, via synthetic and real data experiments, that our approach offers\nadvantages over the classical SPCP formulations in scalability and practical\nparameter selection.","url_abs":"http://arxiv.org/abs/1406.1089v1","url_pdf":"http://arxiv.org/pdf/1406.1089v1.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":"a-variational-approach-to-stable-principal","repo_url":"https://github.com/stephenbeckr/fastRPCA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"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}