{"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/diverse-online-feature-selection","title":"Diverse Online Feature Selection","arxiv_id":"1806.04308","date":"2018-06-12","proceeding":null,"authors":["Chapman Siu","Richard Yi Da Xu"],"abstract":"Online feature selection has been an active research area in recent years. We\npropose a novel diverse online feature selection method based on Determinantal\nPoint Processes (DPP). Our model aims to provide diverse features which can be\ncomposed in either a supervised or unsupervised framework. The framework aims\nto promote diversity based on the kernel produced on a feature level, through\nat most three stages: feature sampling, local criteria and global criteria for\nfeature selection. In the feature sampling, we sample incoming stream of\nfeatures using conditional DPP. The local criteria is used to assess and select\nstreamed features (i.e. only when they arrive), we use unsupervised scale\ninvariant methods to remove redundant features and optionally supervised\nmethods to introduce label information to assess relevant features. Lastly, the\nglobal criteria uses regularization methods to select a global optimal subset\nof features. This three stage procedure continues until there are no more\nfeatures arriving or some predefined stopping condition is met. We demonstrate\nbased on experiments conducted on that this approach yields better compactness,\nis comparable and in some instances outperforms other state-of-the-art online\nfeature selection methods.","url_abs":"http://arxiv.org/abs/1806.04308v3","url_pdf":"http://arxiv.org/pdf/1806.04308v3.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":"diverse-online-feature-selection","repo_url":"https://github.com/chappers/diverse-online-feature-selection","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"point-processes","task_name":"Point Processes"},{"task_slug":"feature-selection","task_name":"feature selection"}],"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}