A Slice Sampler for Restricted Hierarchical Beta Process with Applications to Shared Subspace Learning
This enables more flexible hierarchical modeling for multi-source data, though it appears incremental as an extension of existing hierarchical beta process methods.
The paper tackles the problem of modeling multiple data sources with shared factors by introducing a modified hierarchical beta process prior and deriving a slice sampler for tractable inference. It demonstrates encouraging transfer learning results on text modeling and content-based image retrieval applications.
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.