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A slice sampler for restricted hierarchical beta process with applications to shared subspace learning
conference contribution
posted on 2012-01-01, 00:00 authored by Sunil GuptaSunil Gupta, Quoc-Dinh Phung, Svetha VenkateshSvetha VenkateshHierarchical 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.
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Event
Uncertainty in Artificial Intelligence. Conference (28th : 2012 : Catalina Island, California)Pagination
316 - 325Publisher
AUAI PressLocation
Catalina Island, CaliforniaPlace of publication
Corvallis, Or.Start date
2012-08-15End date
2012-08-17ISBN-13
9780974903989Language
engPublication classification
E1 Full written paper - refereedEditor/Contributor(s)
F de, K MurphyTitle of proceedings
UAI 2012 : Proceedings of the 28th Conference on Uncertainty in Artificial IntelligenceUsage metrics
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