{"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/unsupervised-feature-selection-with-adaptive","title":"Unsupervised Feature Selection with Adaptive Structure Learning","arxiv_id":"1504.00736","date":"2015-04-03","proceeding":null,"authors":["Liang Du","Yi-Dong Shen"],"abstract":"The problem of feature selection has raised considerable interests in the\npast decade. Traditional unsupervised methods select the features which can\nfaithfully preserve the intrinsic structures of data, where the intrinsic\nstructures are estimated using all the input features of data. However, the\nestimated intrinsic structures are unreliable/inaccurate when the redundant and\nnoisy features are not removed. Therefore, we face a dilemma here: one need the\ntrue structures of data to identify the informative features, and one need the\ninformative features to accurately estimate the true structures of data. To\naddress this, we propose a unified learning framework which performs structure\nlearning and feature selection simultaneously. The structures are adaptively\nlearned from the results of feature selection, and the informative features are\nreselected to preserve the refined structures of data. By leveraging the\ninteractions between these two essential tasks, we are able to capture accurate\nstructures and select more informative features. Experimental results on many\nbenchmark data sets demonstrate that the proposed method outperforms many state\nof the art unsupervised feature selection methods.","url_abs":"http://arxiv.org/abs/1504.00736v1","url_pdf":"http://arxiv.org/pdf/1504.00736v1.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":"unsupervised-feature-selection-with-adaptive","repo_url":"https://github.com/csliangdu/FSASL","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"feature-selection","task_name":"feature selection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1504.00736","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}