Beta-negative binomial process and poisson factor analysis
Publication
, Journal Article
Zhou, M; Hannah, LA; Dunson, DB; Carin, L
Published in: Journal of Machine Learning Research
January 1, 2012
A beta-negative binomial (BNB) process is proposed, leading to a beta-gamma-Poisson process, which may be viewed as a "multiscoop" generalization of the beta-Bernoulli process. The BNB process is augmented into a beta-gamma-gamma-Poisson hierarchical structure, and applied as a nonparametric Bayesian prior for an infinite Poisson factor analysis model. A finite approximation for the beta process Lévy random measure is constructed for convenient implementation. Efficient MCMC computations are performed with data augmentation and marginalization techniques. Encouraging results are shown on document count matrix factorization.
Duke Scholars
Published In
Journal of Machine Learning Research
EISSN
1533-7928
ISSN
1532-4435
Publication Date
January 1, 2012
Volume
22
Start / End Page
1462 / 1471
Related Subject Headings
- Artificial Intelligence & Image Processing
- 4905 Statistics
- 4611 Machine learning
- 17 Psychology and Cognitive Sciences
- 08 Information and Computing Sciences
Citation
APA
Chicago
ICMJE
MLA
NLM
Zhou, M., Hannah, L. A., Dunson, D. B., & Carin, L. (2012). Beta-negative binomial process and poisson factor analysis. Journal of Machine Learning Research, 22, 1462–1471.
Zhou, M., L. A. Hannah, D. B. Dunson, and L. Carin. “Beta-negative binomial process and poisson factor analysis.” Journal of Machine Learning Research 22 (January 1, 2012): 1462–71.
Zhou M, Hannah LA, Dunson DB, Carin L. Beta-negative binomial process and poisson factor analysis. Journal of Machine Learning Research. 2012 Jan 1;22:1462–71.
Zhou, M., et al. “Beta-negative binomial process and poisson factor analysis.” Journal of Machine Learning Research, vol. 22, Jan. 2012, pp. 1462–71.
Zhou M, Hannah LA, Dunson DB, Carin L. Beta-negative binomial process and poisson factor analysis. Journal of Machine Learning Research. 2012 Jan 1;22:1462–1471.
Published In
Journal of Machine Learning Research
EISSN
1533-7928
ISSN
1532-4435
Publication Date
January 1, 2012
Volume
22
Start / End Page
1462 / 1471
Related Subject Headings
- Artificial Intelligence & Image Processing
- 4905 Statistics
- 4611 Machine learning
- 17 Psychology and Cognitive Sciences
- 08 Information and Computing Sciences