{"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-subspace-tracking-from-partial","title":"Online Robust Subspace Tracking from Partial Information","arxiv_id":"1109.3827","date":"2011-09-18","proceeding":null,"authors":["Jun He","Laura Balzano","John C. S. Lui"],"abstract":"This paper presents GRASTA (Grassmannian Robust Adaptive Subspace Tracking\nAlgorithm), an efficient and robust online algorithm for tracking subspaces\nfrom highly incomplete information. The algorithm uses a robust $l^1$-norm cost\nfunction in order to estimate and track non-stationary subspaces when the\nstreaming data vectors are corrupted with outliers. We apply GRASTA to the\nproblems of robust matrix completion and real-time separation of background\nfrom foreground in video. In this second application, we show that GRASTA\nperforms high-quality separation of moving objects from background at\nexceptional speeds: In one popular benchmark video example, GRASTA achieves a\nrate of 57 frames per second, even when run in MATLAB on a personal laptop.","url_abs":"http://arxiv.org/abs/1109.3827v2","url_pdf":"http://arxiv.org/pdf/1109.3827v2.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-subspace-tracking-from-partial","repo_url":"https://github.com/hiroyuki-kasai/OLSTEC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"matrix-completion","task_name":"Matrix Completion"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1109.3827","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}