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Hierarchical beta process has found interesting applications in recent years.In this paper we present a modified hierarchical beta process prior withapplications to hierarchical modeling of multiple data sources. The novel useof the prior over a hierarchical factor model allows factors to be sharedacross different sources. We derive a slice sampler for this model, enablingtractable inference even when the likelihood and the prior over parameters arenon-conjugate. This allows the application of the model in much wider contextswithout restrictions. We present two different data generative models a linearGaussianGaussian model for real valued data and a linear Poisson-gamma modelfor count data. Encouraging transfer learning results are shown for two realworld applications text modeling and content based image retrieval.

A Slice Sampler for Restricted Hierarchical Beta Process with Applications to Shared Subspace Learning