{"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/modal-set-estimation-with-an-application-to","title":"Modal-set estimation with an application to clustering","arxiv_id":"1606.04166","date":"2016-06-13","proceeding":null,"authors":["Heinrich Jiang","Samory Kpotufe"],"abstract":"We present a first procedure that can estimate -- with statistical\nconsistency guarantees -- any local-maxima of a density, under benign\ndistributional conditions. The procedure estimates all such local maxima, or\n$\\textit{modal-sets}$, of any bounded shape or dimension, including usual\npoint-modes. In practice, modal-sets can arise as dense low-dimensional\nstructures in noisy data, and more generally serve to better model the rich\nvariety of locally-high-density structures in data.\n  The procedure is then shown to be competitive on clustering applications, and\nmoreover is quite stable to a wide range of settings of its tuning parameter.","url_abs":"http://arxiv.org/abs/1606.04166v1","url_pdf":"http://arxiv.org/pdf/1606.04166v1.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":"modal-set-estimation-with-an-application-to","repo_url":"https://github.com/hhjiang/mcores","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1606.04166","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}