{"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/multi-view-low-rank-sparse-subspace","title":"Multi-view Low-rank Sparse Subspace Clustering","arxiv_id":"1708.08732","date":"2017-08-29","proceeding":null,"authors":["Maria Brbic","Ivica Kopriva"],"abstract":"Most existing approaches address multi-view subspace clustering problem by\nconstructing the affinity matrix on each view separately and afterwards propose\nhow to extend spectral clustering algorithm to handle multi-view data. This\npaper presents an approach to multi-view subspace clustering that learns a\njoint subspace representation by constructing affinity matrix shared among all\nviews. Relying on the importance of both low-rank and sparsity constraints in\nthe construction of the affinity matrix, we introduce the objective that\nbalances between the agreement across different views, while at the same time\nencourages sparsity and low-rankness of the solution. Related low-rank and\nsparsity constrained optimization problem is for each view solved using the\nalternating direction method of multipliers. Furthermore, we extend our\napproach to cluster data drawn from nonlinear subspaces by solving the\ncorresponding problem in a reproducing kernel Hilbert space. The proposed\nalgorithm outperforms state-of-the-art multi-view subspace clustering\nalgorithms on one synthetic and four real-world datasets.","url_abs":"http://arxiv.org/abs/1708.08732v1","url_pdf":"http://arxiv.org/pdf/1708.08732v1.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":"multi-view-low-rank-sparse-subspace","repo_url":"https://github.com/mbrbic/Multi-view-LRSSC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"multi-view-low-rank-sparse-subspace","repo_url":"https://github.com/mbrbic/MultiViewLRSSC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"multi-view-subspace-clustering","task_name":"Multi-view Subspace Clustering"}],"methods":[{"method_slug":"spectral-clustering","method_name":"Spectral Clustering"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1708.08732","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}