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.
History
Event
International Conference on Pattern Recognition (21st : 2012 : Tsukuba Science City, Japan)
Pagination
1815 - 1818
Publisher
ICPR Organizing Committee
Location
Tsubuka Science City, Japan
Place of publication
Tsubuka Science City, Japan
Start date
2012-11-11
End date
2012-11-15
ISBN-13
9784990644109
Language
eng
Publication classification
E1 Full written paper - refereed
Title of proceedings
ICPR 2012 : Proceedings of 21st International Conference on Pattern Recognition