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Centre for Pattern Recognition and Data Analytics
School of Information Technology
Deakin University
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S.K. Gupta, D. Phung, and S. Venkatesh. A slice sampler for restricted hierarchical beta process with applications to shared subspace learning. In Proceedings of 28th Uncertainty in Artificial Intelligence (UAI), pages 316-325, 2012.

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.

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19th February 2015