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Bayesian nonparametric multilevel clustering with group-level contexts

Nguyen, Vu, Phung, Dinh, Nguyen, XuanLong, Venkatesh, Svetha and Bui, Hung Hai 2014, Bayesian nonparametric multilevel clustering with group-level contexts, in ICML 2014 : Proceedings of the 31st International Conference on Machine Learning, International Machine Learning Society (IMLS), [Berlin, Germany], pp. 288-296.

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Title Bayesian nonparametric multilevel clustering with group-level contexts
Author(s) Nguyen, Vu
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Nguyen, XuanLong
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Bui, Hung Hai
Conference name Machine Learning. Conference (31st : 2014 : Beijing, China)
Conference location Beijing, China
Conference dates 21-26 Jun 2016
Title of proceedings ICML 2014 : Proceedings of the 31st International Conference on Machine Learning
Publication date 2014
Series Proceedings of Machine Learning Research, v. 32
Conference series International Conference on Machine Learning
Start page 288
End page 296
Total pages 9
Publisher International Machine Learning Society (IMLS)
Place of publication [Berlin, Germany]
Summary 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.
ISBN 9781634393973
ISSN 1938-7228
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2014, The Authors
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30072272

Document type: Conference Paper
Collections: Centre for Pattern Recognition and Data Analytics
Open Access Collection
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.