{"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/collaborative-low-rank-subspace-clustering","title":"Collaborative Low-Rank Subspace Clustering","arxiv_id":"1704.03966","date":"2017-04-13","proceeding":null,"authors":["Stephen Tierney","Yi Guo","Junbin Gao"],"abstract":"In this paper we present Collaborative Low-Rank Subspace Clustering. Given\nmultiple observations of a phenomenon we learn a unified representation matrix.\nThis unified matrix incorporates the features from all the observations, thus\nincreasing the discriminative power compared with learning the representation\nmatrix on each observation separately. Experimental evaluation shows that our\nmethod outperforms subspace clustering on separate observations and the state\nof the art collaborative learning algorithm.","url_abs":"http://arxiv.org/abs/1704.03966v1","url_pdf":"http://arxiv.org/pdf/1704.03966v1.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":"collaborative-low-rank-subspace-clustering","repo_url":"https://github.com/sjtrny/collab_lrsc","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}