{"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/medoids-in-almost-linear-time-via-multi-armed","title":"Medoids in almost linear time via multi-armed bandits","arxiv_id":"1711.00817","date":"2017-11-02","proceeding":null,"authors":["Vivek Bagaria","Govinda M. Kamath","Vasilis Ntranos","Martin J. Zhang","David Tse"],"abstract":"Computing the medoid of a large number of points in high-dimensional space is\nan increasingly common operation in many data science problems. We present an\nalgorithm Med-dit which uses O(n log n) distance evaluations to compute the\nmedoid with high probability. Med-dit is based on a connection with the\nmulti-armed bandit problem. We evaluate the performance of Med-dit empirically\non the Netflix-prize and the single-cell RNA-Seq datasets, containing hundreds\nof thousands of points living in tens of thousands of dimensions, and observe a\n5-10x improvement in performance over the current state of the art. Med-dit is\navailable at https://github.com/bagavi/Meddit","url_abs":"http://arxiv.org/abs/1711.00817v3","url_pdf":"http://arxiv.org/pdf/1711.00817v3.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":"medoids-in-almost-linear-time-via-multi-armed","repo_url":"https://github.com/bagavi/Meddit","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"multi-armed-bandits","task_name":"Multi-Armed Bandits"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1711.00817","atlas_url":"https://app.syntology.ai/?focus=1711.00817","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}