A Slice Sampler for Restricted Hierarchical Beta Process with Applications to Shared Subspace Learning
Sunil Gupta, Dinh Phung, Svetha Venkatesh
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.
PDF Link: /papers/12/p316-gupta.pdf
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"