{"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/multilevel-clustering-via-wasserstein-means","title":"Multilevel Clustering via Wasserstein Means","arxiv_id":"1706.03883","date":"2017-06-13","proceeding":"ICML 2017 8","authors":["Nhat Ho","XuanLong Nguyen","Mikhail Yurochkin","Hung Hai Bui","Viet Huynh","Dinh Phung"],"abstract":"We propose a novel approach to the problem of multilevel clustering, which\naims to simultaneously partition data in each group and discover grouping\npatterns among groups in a potentially large hierarchically structured corpus\nof data. Our method involves a joint optimization formulation over several\nspaces of discrete probability measures, which are endowed with Wasserstein\ndistance metrics. We propose a number of variants of this problem, which admit\nfast optimization algorithms, by exploiting the connection to the problem of\nfinding Wasserstein barycenters. Consistency properties are established for the\nestimates of both local and global clusters. Finally, experiment results with\nboth synthetic and real data are presented to demonstrate the flexibility and\nscalability of the proposed approach.","url_abs":"http://arxiv.org/abs/1706.03883v1","url_pdf":"http://arxiv.org/pdf/1706.03883v1.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":"multilevel-clustering-via-wasserstein-means","repo_url":"https://github.com/moonfolk/Multilevel-Wasserstein-Means","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1706.03883","atlas_url":"https://app.syntology.ai/?focus=1706.03883","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}