A nonparametric Bayesian Poisson Gamma model for count data

Gupta, Sunil Kumar, Phung, Dinh and Venkatesh, Svetha 2012, A nonparametric Bayesian Poisson Gamma model for count data, in ICPR 2012 : Proceedings of 21st International Conference on Pattern Recognition, ICPR Organizing Committee, Tsubuka Science City, Japan, pp. 1815-1818.

Attached Files
Name Description MIMEType Size Downloads

Title A nonparametric Bayesian Poisson Gamma model for count data
Author(s) Gupta, Sunil KumarORCID iD for Gupta, Sunil Kumar orcid.org/0000-0002-3308-1930
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Conference name International Conference on Pattern Recognition (21st : 2012 : Tsukuba Science City, Japan)
Conference location Tsubuka Science City, Japan
Conference dates 11-15 Nov. 2012
Title of proceedings ICPR 2012 : Proceedings of 21st International Conference on Pattern Recognition
Editor(s) [Unknown]
Publication date 2012
Conference series International Conference on Pattern Recognition
Start page 1815
End page 1818
Total pages 4
Publisher ICPR Organizing Committee
Place of publication Tsubuka Science City, Japan
Summary 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.
ISBN 9784990644109
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
080110 Simulation and Modelling
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category E1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30052644

Connect to link resolver
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 5 times in TR Web of Science
Scopus Citation Count Cited 8 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 714 Abstract Views, 12 File Downloads  -  Detailed Statistics
Created: Fri, 24 May 2013, 12:10:27 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.