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Bayesian nonparametric multilevel clustering with group-level contexts
conference contribution
posted on 2014-01-01, 00:00 authored by Tien Vu Nguyen, Quoc-Dinh Phung, X L Nguyen, Svetha VenkateshSvetha Venkatesh, H H BuiWe 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.
History
Event
Machine Learning. Conference (31st : 2014 : Beijing, China)Volume
32Issue
1Series
Proceedings of Machine Learning ResearchPagination
288 - 269Publisher
International Machine Learning Society (IMLS)Location
Beijing, ChinaPlace of publication
[Berlin, Germany]Start date
2014-06-21End date
2014-06-26ISBN-13
9781634393973Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2014, The AuthorsEditor/Contributor(s)
[Unknown]Title of proceedings
ICML 2014 : Proceedings of the 31st International Conference on Machine LearningUsage metrics
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