Multilevel clustering via wasserstein means

Nhat Ho, XuanLong Nguyen, Mikhail Yurochkin, Hung Hai Bui, Viet Huynh, Dinh Phung

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

8 Citations (Scopus)


We propose a novel approach to the problem of multilevel clustering, which aims to simultaneously partition data in each group and discover grouping patterns among groups in a potentially large hierarchically structured corpus of data. Our method involves a joint optimization formulation over several spaces of discrete probability measures, which are endowed with Wasserstein distance metrics. We propose a number of variants of this problem, which admit fast optimization algorithms, by exploiting the connection to the problem of finding Wasserstein barycenters. Consistency properties are established for the estimates of both local and global clusters. Finally, experiment results with both synthetic and real data arc presented to demonstrate the flexibility and scalability of the proposed approach.

Original languageEnglish
Title of host publication34th International Conference on Machine Learning (ICML 2017)
Subtitle of host publicationSydney, Australia 6 - 11 August 2017
EditorsDoina Precup, Yee Whye Teh
Place of PublicationStroudsburg PA USA
PublisherInternational Machine Learning Society (IMLS)
Number of pages15
ISBN (Electronic)9781510855144
Publication statusPublished - 2017
Externally publishedYes
EventInternational Conference on Machine Learning 2017 - International Convention Centre , Sydney , Australia
Duration: 6 Aug 201711 Aug 2018
Conference number: 34th


ConferenceInternational Conference on Machine Learning 2017
Abbreviated titleICML 2017
Internet address

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