File(s) under permanent embargo
A nonparametric Bayesian Poisson Gamma model for count data
conference contributionposted on 2012-01-01, 00:00 authored by Sunil GuptaSunil Gupta, Quoc-Dinh Phung, Svetha VenkateshSvetha Venkatesh
We propose a nonparametric Bayesian, linear Poisson gamma model for count data and use it for dictionary learning. A key property of this model is that it captures the parts-based representation similar to nonnegative matrix factorization. We present an auxiliary variable Gibbs sampler, which turns the intractable inference into a tractable one. Combining this inference procedure with the slice sampler of Indian buffet process, we show that our model can learn the number of factors automatically. Using synthetic and real-world datasets, we show that the proposed model outperforms other state-of-the-art nonparametric factor models.