{"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/large-scale-correlation-clustering","title":"Large Scale Correlation Clustering Optimization","arxiv_id":"1112.2903","date":"2011-12-13","proceeding":null,"authors":["Shai Bagon","Meirav Galun"],"abstract":"Clustering is a fundamental task in unsupervised learning. The focus of this\npaper is the Correlation Clustering functional which combines positive and\nnegative affinities between the data points. The contribution of this paper is\ntwo fold: (i) Provide a theoretic analysis of the functional. (ii) New\noptimization algorithms which can cope with large scale problems (>100K\nvariables) that are infeasible using existing methods. Our theoretic analysis\nprovides a probabilistic generative interpretation for the functional, and\njustifies its intrinsic \"model-selection\" capability. Furthermore, we draw an\nanalogy between optimizing this functional and the well known Potts energy\nminimization. This analogy allows us to suggest several new optimization\nalgorithms, which exploit the intrinsic \"model-selection\" capability of the\nfunctional to automatically recover the underlying number of clusters. We\ncompare our algorithms to existing methods on both synthetic and real data. In\naddition we suggest two new applications that are made possible by our\nalgorithms: unsupervised face identification and interactive multi-object\nsegmentation by rough boundary delineation.","url_abs":"http://arxiv.org/abs/1112.2903v1","url_pdf":"http://arxiv.org/pdf/1112.2903v1.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":"large-scale-correlation-clustering","repo_url":"https://github.com/shaibagon/large_scale_cc","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"large-scale-correlation-clustering","repo_url":"https://github.com/nveldt/LamCC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"large-scale-correlation-clustering","repo_url":"https://github.com/Recognito-Vision/Android-FaceRecognition-FaceLivenessDetection","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":"face-identification","task_name":"Face Identification"},{"task_slug":"model-selection","task_name":"Model Selection"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}