Uncertainty in Artificial Intelligence
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A Slice Sampler for Restricted Hierarchical Beta Process with Applications to Shared Subspace Learning
Sunil Gupta, Dinh Phung, Svetha Venkatesh
Abstract:
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 GaussianGaussian 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.
Keywords:
Pages: 316-325
PS Link:
PDF Link: /papers/12/p316-gupta.pdf
BibTex:
@INPROCEEDINGS{Gupta12,
AUTHOR = "Sunil Gupta and Dinh Phung and Svetha Venkatesh",
TITLE = "A Slice Sampler for Restricted Hierarchical Beta Process with Applications to Shared Subspace Learning",
BOOKTITLE = "Proceedings of the Twenty-Eighth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-12)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2012",
PAGES = "316--325"
}


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