{"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/variational-information-maximization-for","title":"Variational Information Maximization for Feature Selection","arxiv_id":"1606.02827","date":"2016-06-09","proceeding":"NeurIPS 2016 12","authors":["Shuyang Gao","Greg Ver Steeg","Aram Galstyan"],"abstract":"Feature selection is one of the most fundamental problems in machine\nlearning. An extensive body of work on information-theoretic feature selection\nexists which is based on maximizing mutual information between subsets of\nfeatures and class labels. Practical methods are forced to rely on\napproximations due to the difficulty of estimating mutual information. We\ndemonstrate that approximations made by existing methods are based on\nunrealistic assumptions. We formulate a more flexible and general class of\nassumptions based on variational distributions and use them to tractably\ngenerate lower bounds for mutual information. These bounds define a novel\ninformation-theoretic framework for feature selection, which we prove to be\noptimal under tree graphical models with proper choice of variational\ndistributions. Our experiments demonstrate that the proposed method strongly\noutperforms existing information-theoretic feature selection approaches.","url_abs":"http://arxiv.org/abs/1606.02827v1","url_pdf":"http://arxiv.org/pdf/1606.02827v1.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":"variational-information-maximization-for","repo_url":"https://github.com/BiuBiuBiLL/InfoFeatureSelection","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"feature-selection","task_name":"feature selection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1606.02827","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}