{"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/robust-graph-learning-from-noisy-data","title":"Robust Graph Learning from Noisy Data","arxiv_id":"1812.06673","date":"2018-12-17","proceeding":null,"authors":["Zhao Kang","Haiqi Pan","Steven C. H. Hoi","Zenglin Xu"],"abstract":"Learning graphs from data automatically has shown encouraging performance on\nclustering and semisupervised learning tasks. However, real data are often\ncorrupted, which may cause the learned graph to be inexact or unreliable. In\nthis paper, we propose a novel robust graph learning scheme to learn reliable\ngraphs from real-world noisy data by adaptively removing noise and errors in\nthe raw data. We show that our proposed model can also be viewed as a robust\nversion of manifold regularized robust PCA, where the quality of the graph\nplays a critical role. The proposed model is able to boost the performance of\ndata clustering, semisupervised classification, and data recovery\nsignificantly, primarily due to two key factors: 1) enhanced low-rank recovery\nby exploiting the graph smoothness assumption, 2) improved graph construction\nby exploiting clean data recovered by robust PCA. Thus, it boosts the\nclustering, semi-supervised classification, and data recovery performance\noverall. Extensive experiments on image/document clustering, object\nrecognition, image shadow removal, and video background subtraction reveal that\nour model outperforms the previous state-of-the-art methods.","url_abs":"http://arxiv.org/abs/1812.06673v1","url_pdf":"http://arxiv.org/pdf/1812.06673v1.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":"robust-graph-learning-from-noisy-data","repo_url":"https://github.com/sckangz/RGC","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"robust-graph-learning-from-noisy-data","repo_url":"https://github.com/FaceOnLive/Realtime-Background-Changer-SDK-Android","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"graph-learning","task_name":"Graph Learning"},{"task_slug":"image-shadow-removal","task_name":"Image Shadow Removal"},{"task_slug":"imagedocument-clustering","task_name":"Image/Document Clustering"},{"task_slug":"object-recognition","task_name":"Object Recognition"},{"task_slug":"shadow-removal","task_name":"Shadow Removal"},{"task_slug":"video-background-subtraction","task_name":"Video Background Subtraction"},{"task_slug":"graph-construction","task_name":"graph construction"}],"methods":[{"method_slug":"pca","method_name":"PCA"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1812.06673","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}