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A nonparametric Bayesian Poisson Gamma model for count data
conference contribution
posted on 2012-01-01, 00:00 authored by Sunil GuptaSunil Gupta, Quoc-Dinh Phung, Svetha VenkateshSvetha VenkateshWe 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.
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International Conference on Pattern Recognition (21st : 2012 : Tsukuba Science City, Japan)Pagination
1815 - 1818Publisher
ICPR Organizing CommitteeLocation
Tsubuka Science City, JapanPlace of publication
Tsubuka Science City, JapanStart date
2012-11-11End date
2012-11-15ISBN-13
9784990644109Language
engPublication classification
E1 Full written paper - refereedTitle of proceedings
ICPR 2012 : Proceedings of 21st International Conference on Pattern RecognitionUsage metrics
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