{"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/online-robust-principal-component-analysis","title":"Online Robust Principal Component Analysis with Change Point Detection","arxiv_id":"1702.05698","date":"2017-02-19","proceeding":null,"authors":["Wei Xiao","Xiaolin Huang","Jorge Silva","Saba Emrani","Arin Chaudhuri"],"abstract":"Robust PCA methods are typically batch algorithms which requires loading all\nobservations into memory before processing. This makes them inefficient to\nprocess big data. In this paper, we develop an efficient online robust\nprincipal component methods, namely online moving window robust principal\ncomponent analysis (OMWRPCA). Unlike existing algorithms, OMWRPCA can\nsuccessfully track not only slowly changing subspace but also abruptly changed\nsubspace. By embedding hypothesis testing into the algorithm, OMWRPCA can\ndetect change points of the underlying subspaces. Extensive simulation studies\ndemonstrate the superior performance of OMWRPCA compared with other\nstate-of-art approaches. We also apply the algorithm for real-time background\nsubtraction of surveillance video.","url_abs":"http://arxiv.org/abs/1702.05698v2","url_pdf":"http://arxiv.org/pdf/1702.05698v2.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":"online-robust-principal-component-analysis","repo_url":"https://github.com/wxiao0421/onlineRPCA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"online-robust-principal-component-analysis","repo_url":"https://github.com/wxiao0421/onlineRPCA-matlab","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"change-point-detection","task_name":"Change Point Detection"},{"task_slug":"hypothesis-testing","task_name":"Two-sample testing"}],"methods":[{"method_slug":"pca","method_name":"PCA"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}