{"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/efficient-sparse-subspace-clustering-by","title":"Efficient Sparse Subspace Clustering by Nearest Neighbour Filtering","arxiv_id":"1704.03958","date":"2017-04-13","proceeding":null,"authors":["Stephen Tierney","Yi Guo","Junbin Gao"],"abstract":"Sparse Subspace Clustering (SSC) has been used extensively for subspace\nidentification tasks due to its theoretical guarantees and relative ease of\nimplementation. However SSC has quadratic computation and memory requirements\nwith respect to the number of input data points. This burden has prohibited\nSSCs use for all but the smallest datasets. To overcome this we propose a new\nmethod, k-SSC, that screens out a large number of data points to both reduce\nSSC to linear memory and computational requirements. We provide theoretical\nanalysis for the bounds of success for k-SSC. Our experiments show that k-SSC\nexceeds theoretical expectations and outperforms existing SSC approximations by\nmaintaining the classification performance of SSC. Furthermore in the spirit of\nreproducible research we have publicly released the source code for k-SSC","url_abs":"http://arxiv.org/abs/1704.03958v1","url_pdf":"http://arxiv.org/pdf/1704.03958v1.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":"efficient-sparse-subspace-clustering-by","repo_url":"https://github.com/sjtrny/kssc","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}