Bayesian nonparametric multilevel clustering with group-level contexts

Vu Nguyen, Dinh Phung, XuanLong Nguyen, Svetha Venkatesh, Hung Hal Bui

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

18 Citations (Scopus)


2014 We present a Bayesian nonparametric framework for multilevel clustering which utilizes group- level context information to simultaneously discover low-dimensional structures of the group contents and partitions groups into clusters. Using the Dirichlet process as the building block, our model constructs a product base-measure with a nested structure to accommodate content and context observations at multiple levels. The proposed model possesses properties that link the nested Dinchiet processes (nDP) and the Dirichlet process mixture models (DPM) in an interesting way: integrating out all contents results in the DPM over contexts, whereas integrating out group-specific contexts results in the nDP mixture over content variables. We provide a Polyaurn view of the model and an efficient collapsed Gibbs inference procedure. Extensive experiments on real-world datasets demonstrate the advantage of utilizing context information via our model in both text and image domains.

Original languageEnglish
Title of host publication31st International Conference on Machine Learning - (ICML 2014)
Subtitle of host publicationBeijing, China 21-26 June 2014 - Volume 1 of 5
EditorsEric P. Xing, Tony Jebara
Place of PublicationRed Hook NY USA
PublisherInternational Machine Learning Society (IMLS)
Number of pages21
ISBN (Electronic)9781634393973
Publication statusPublished - 2014
Externally publishedYes
EventInternational Conference on Machine Learning 2014 - Beijing International Convention Center (BICC), Beijing, China
Duration: 21 Jun 201426 Jun 2014
Conference number: 31st


ConferenceInternational Conference on Machine Learning 2014
Abbreviated titleICML 2014
Internet address

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