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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.

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Title A slice sampler for restricted hierarchical beta process with applications to shared subspace learning
Author(s) Gupta, Sunil KumarORCID iD for Gupta, Sunil Kumar orcid.org/0000-0002-3308-1930
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Venkatesh, SvethaORCID iD for 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
Collection: Centre for Pattern Recognition and Data Analytics
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