Title :
A nonparametric Bayesian Poisson gamma model for count data
Author :
Gupta, Suneet K. ; Dinh Phung ; Venkatesh, Svetha
Author_Institution :
Centre for Pattern Recognition & Data Analytics, Deakin Univ., Geelong, VIC, Australia
Abstract :
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.
Keywords :
Bayes methods; image recognition; inference mechanisms; learning (artificial intelligence); matrix decomposition; nonparametric statistics; stochastic processes; Indian buffet process; auxiliary variable Gibbs sampler; count data; dictionary learning; intractable inference procedure; nonnegative matrix factorization; nonparametric Bayesian Poisson gamma model; nonparametric factor model; parts-based representation; slice sampler; Analytical models; Bayesian methods; Data models; Dictionaries; Face; Indexes; Load modeling;
Conference_Titel :
Pattern Recognition (ICPR), 2012 21st International Conference on
Conference_Location :
Tsukuba
Print_ISBN :
978-1-4673-2216-4