A slice sampler for restricted hierarchical beta process with applications to shared subspace learning Gupta, Sunil Kumar, Phung, Dinh and Venkatesh, Svetha 2012, A slice sampler for restricted hierarchical beta process with applications to shared subspace learning, in UAI 2012 : Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, AUAI Press, Corvallis, Or., pp. 316-325. Attached Files Name Description MIMEType Size Downloads Title A slice sampler for restricted hierarchical beta process with applications to shared subspace learning Author(s) Gupta, Sunil Kumar orcid.org/0000-0002-3308-1930 Phung, Dinh orcid.org/0000-0002-9977-8247 Venkatesh, Svetha orcid.org/0000-0001-8675-6631 Conference name Uncertainty in Artificial Intelligence. Conference (28th : 2012 : Catalina Island, California) Conference location Catalina Island, California Conference dates 15-17 Aug. 2012 Title of proceedings UAI 2012 : Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence Editor(s) de, Freitas Nando Murphy, Kevin Publication date 2012 Conference series Conference on Uncertainty in Artificial Intelligence Start page 316 End page 325 Total pages 10 Publisher AUAI Press Place of publication Corvallis, Or. Summary Hierarchical beta process has found interesting applications in recent years. In this paper we present a modified hierarchical beta process prior with applications to hierarchical modeling of multiple data sources. The novel use of the prior over a hierarchical factor model allows factors to be shared across different sources. We derive a slice sampler for this model, enabling tractable inference even when the likelihood and the prior over parameters are non-conjugate. This allows the application of the model in much wider contexts without restrictions. We present two different data generative models – a linear Gaussian-Gaussian model for real valued data and a linear Poisson-gamma model for count data. Encouraging transfer learning results are shown for two real world applications – text modeling and content based image retrieval. ISBN 9780974903989 Language eng Field of Research 080109 Pattern Recognition and Data Mining 080110 Simulation and Modelling Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture) HERDC Research category E1 Full written paper - refereed Persistent URL http://hdl.handle.net/10536/DRO/DU:30052648 Document type: Conference Paper Collections: Centre for Pattern Recognition and Data Analytics 2018 ERA Submission Related Links Link Description Connect to published version (restricted access) 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 Fri, 24 May 2013, 12:11:22 EST Fri, 24 May 2013, 14:27:01 EST Fri, 24 May 2013, 14:29:41 EST Fri, 24 May 2013, 15:31:23 EST Fri, 24 May 2013, 15:34:20 EST Mon, 27 May 2013, 08:42:10 EST Mon, 27 May 2013, 14:05:34 EST Mon, 27 May 2013, 14:07:23 EST Mon, 10 Jun 2013, 10:32:29 EST Wed, 21 Oct 2015, 14:47:18 EST Thu, 09 Jun 2016, 14:05:51 EST Thu, 09 Jun 2016, 14:06:24 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 6 times in Scopus Search Google Scholar Access Statistics: 350 Abstract Views, 4 File Downloads  -  Detailed Statistics Created: Fri, 24 May 2013, 12:11:22 EST

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