Reducing the sampling complexity of topic models

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2014, Pages 891-900

  Li, Aaron Q ^a   Ahmed, Amr ^b   Ravi, Sujith ^b   Smola, Alexander J ^a,b  

^a CARNEGIE MELLON UNIVERSITY   (United States)

^b GOOGLE INC   (United States)

Author keywords

alias method; sampling; scalability; topic models

Indexed keywords

EID: 84907030865     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2623330.2623756     Document Type: Conference Paper

References (22)

1. K. Bache and M. Lichman. UCI machine learning repository, 2013.
2. D. Blei, T. Griffiths, and M. Jordan. The nested chinese restaurant process and Bayesian nonparametric inference of topic hierarchies. Journal of the ACM, 57(2):1-30, 2010.
3. D. Blei, A. Ng, and M. Jordan. Latent Dirichlet allocation. JMLR, 3:993-1022, Jan. 2003.
4. W. Buntine and M. Hutter. A bayesian review of the poisson-dirichlet process, 2010.
5. C. Chen, W. Buntine, N. Ding, L. Xie, and L. Du. Differential topic models. In IEEE Pattern Analysis and Machine Intelligence, 2014.
6. C. Chen, L. Du, and W. Buntine. Sampling table configurations for the hierarchical poisson-dirichlet process. In ECML, pages 296-311, 2011.
7. J. Geweke and H. Tanizaki. Bayesian estimation of state-space model using the metropolis-hastings algorithm within gibbs sampling. Computational Statistics and Data Analysis, 37(2):151-170, 2001.
8. W. R. Gilks, S. Richardson, and D. J. Spiegelhalter. Markov Chain Monte Carlo in Practice. 1995.
9. T. Griffiths and M. Steyvers. Finding scientific topics. PNAS, 101:5228-5235, 2004.
10. G. Heinrich. Parameter estimation for text analysis. Technical report, Fraunhofer IGD, 2004.
11. M. Hoffman, D. M. Blei, C. Wang, and J. Paisley.Stochastic variational inference. In ICML, 2012.
12. W. Li, D. Blei, and A. McCallum. Nonparametric bayes pachinko allocation. In UAI, 2007.
13. G. Marsaglia, W. W. Tsang, and J. Wang. Fast generation of discrete random variables. Journal of Statistical Software, 11(3):1-8, 2004.
14. R. M. Neal. Markov chain sampling methods for Dirichlet process mixture models. University of Toronto, Technical Report 9815, 1998.
15. J. Petterson, A. Smola, T. Caetano, W. Buntine, and S. Narayanamurthy. Word features for latent dirichlet allocation. In NIPS, pages 1921-1929, 2010.
16. J. Pitman and M. Yor. The two-parameter poisson-dirichlet distribution derived from a stable subordinator. A. of Probability, 25(2):855-900, 1997.
17. I. Sato and H. Nakagawa. Topic models withpower-law using Pitman-Yor process. In KDD, pages 673-682. ACM, 2010.
18. A. J. Smola and S. Narayanamurthy. An architecture for parallel topic models. In PVLDB, 2010.
19. Y. Teh, M. Jordan, M. Beal, and D. Blei. Hierarchical dirichlet processes. JASA, 101(576):1566-1581, 2006.
20. A. J. Walker. An efficient method for generating discrete random variables with general distributions. ACM TOMS, 3(3):253-256, 1977.
21. C. Wang, J. Paisley, and D. M. Blei. Online variational inference for the hierarchical Dirichlet process. In Conference on Artificial Intelligence andStatistics, 2011.
22. L. Yao, D. Mimno, and A. McCallum. Efficientmethods for topic model inference on streaming document collections. In KDD'09, 2009.