{"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/an-empirical-bayes-approach-for-high","title":"An Empirical Bayes Approach for High Dimensional Classification","arxiv_id":"1702.05056","date":"2017-02-16","proceeding":null,"authors":["Yunbo Ouyang","Feng Liang"],"abstract":"We propose an empirical Bayes estimator based on Dirichlet process mixture\nmodel for estimating the sparse normalized mean difference, which could be\ndirectly applied to the high dimensional linear classification. In theory, we\nbuild a bridge to connect the estimation error of the mean difference and the\nmisclassification error, also provide sufficient conditions of sub-optimal\nclassifiers and optimal classifiers. In implementation, a variational Bayes\nalgorithm is developed to compute the posterior efficiently and could be\nparallelized to deal with the ultra-high dimensional case.","url_abs":"http://arxiv.org/abs/1702.05056v1","url_pdf":"http://arxiv.org/pdf/1702.05056v1.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":"an-empirical-bayes-approach-for-high","repo_url":"https://github.com/yunboouyang/EBclassifier","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"high","task_name":"Vocal Bursts Intensity Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}