{"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/tractable-clustering-of-data-on-the-curve","title":"Tractable Clustering of Data on the Curve Manifold","arxiv_id":"1704.03963","date":"2017-04-13","proceeding":null,"authors":["Stephen Tierney","Junbin Gao","Yi Guo","Zheng Zhang"],"abstract":"In machine learning it is common to interpret each data point as a vector in\nEuclidean space. However the data may actually be functional i.e.\\ each data\npoint is a function of some variable such as time and the function is\ndiscretely sampled. The naive treatment of functional data as traditional\nmultivariate data can lead to poor performance since the algorithms are\nignoring the correlation in the curvature of each function. In this paper we\npropose a tractable method to cluster functional data or curves by adapting the\nEuclidean Low-Rank Representation (LRR) to the curve manifold. Experimental\nevaluation on synthetic and real data reveals that this method massively\noutperforms prior clustering methods in both speed and accuracy when clustering\nfunctional data.","url_abs":"http://arxiv.org/abs/1704.03963v1","url_pdf":"http://arxiv.org/pdf/1704.03963v1.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":"tractable-clustering-of-data-on-the-curve","repo_url":"https://github.com/sjtrny/curveLRR","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}