A Slice Sampler for Restricted Hierarchical Beta Process with Applications to Shared Subspace Learning
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.
Cite
@article{arxiv.1210.4855,
title = {A Slice Sampler for Restricted Hierarchical Beta Process with Applications to Shared Subspace Learning},
author = {Sunil Kumar Gupta and Dinh Q. Phung and Svetha Venkatesh},
journal= {arXiv preprint arXiv:1210.4855},
year = {2012}
}
Comments
Appears in Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (UAI2012)