Scalable nonparametric Bayesian multilevel clustering Huynh, Viet, Phung, Dinh, Venkatesh, Svetha, Nguyen, Xuan Long, Hoffman, Matt and Bui, Hung Hai 2016, Scalable nonparametric Bayesian multilevel clustering, in UAI 2016: Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence, AUAI Press, Corvallis, Or., pp. 289-298. Attached Files Name Description MIMEType Size Downloads Title Scalable nonparametric Bayesian multilevel clustering Author(s) Huynh, Viet Phung, Dinh orcid.org/0000-0002-9977-8247 Venkatesh, Svetha Nguyen, Xuan Long Hoffman, Matt Bui, Hung Hai Conference name Uncertainty in Artificial Intelligence. Conference (32nd : 2016 : New York, N.Y.) Conference location New York, N.Y. Conference dates 25-29 Jun. 2016 Title of proceedings UAI 2016: Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence Editor(s) Ihler, A. Janzing, D. Publication date 2016 Start page 289 End page 298 Total pages 10 Publisher AUAI Press Place of publication Corvallis, Or. Summary Multilevel clustering problems where the con-tent and contextual information are jointly clustered are ubiquitous in modern datasets. Existing works on this problem are limited to small datasets due to the use of the Gibbs sampler. We address the problem of scaling up multi-level clustering under a Bayesian nonparametric setting, extending the MC2 model proposed in (Nguyen et al., 2014). We ground our approach in structured mean-field and stochastic variational inference (SVI) and develop a tree-structured SVI algorithm that exploits the interplay between content and context modeling. Our new algorithm avoids the need to repeatedly go through the corpus as in Gibbs sampler. More crucially, our method is immediately amendable to parallelization, facilitating a scalable distributed implementation on the Apache Spark platform. We conduct extensive experiments in a variety of domains including text, images, and real-world user application activities. Direct comparison with the Gibbs-sampler demonstrates that our method is an order-of-magnitude faster without loss of model quality. Our Spark-based implementation gains an-other order-of-magnitude speedup and can scale to large real-world datasets containing millions of documents and groups. ISBN 9780996643115 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 ©2016, AUAI Press Persistent URL http://hdl.handle.net/10536/DRO/DU:30085090 Document type: Conference Paper Collection: Centre for Pattern Recognition and Data Analytics Related Links Link Description Connect to conference website Go to link with your DU access privileges   Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Tue, 26 Jul 2016, 17:06:49 EST Tue, 26 Jul 2016, 17:11:42 EST Wed, 27 Jul 2016, 05:00:14 EST Wed, 27 Jul 2016, 06:11:20 EST Thu, 04 Aug 2016, 14:52:26 EST Thu, 04 Aug 2016, 14:53:09 EST Wed, 12 Oct 2016, 15:44:19 EST Wed, 11 Jan 2017, 09:59:42 EST Wed, 01 Mar 2017, 08:16:31 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 122 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Tue, 26 Jul 2016, 17:11:42 EST

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