Joint analysis of time-evolving binary matrices and associated documents

Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010

Volumn , Issue , 2010, Pages

  Wang, Eric ^a   Liu, Dehong ^a   Silva, Jorge ^a   Dunson, David ^b   Carin, Lawrence ^a  

^a Duke University   (United States)

^b DUKE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE;

ANALYSIS MODELS; BAYESIAN PERSPECTIVE; BINARY MATRIX; GIBBS SAMPLING; INTER-RELATIONSHIPS; JOINT ANALYSIS; MACHINE-LEARNING; MATRIX; MATRIX ANALYSIS; TOPIC MODELING;

LEARNING SYSTEMS;

EID: 85162010864     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. J. Abernethy, F. Bach, T. Evgeniou, and J.-P. Vert. A new approach to collaborative filtering: operator estimation with spectral regularization. J. Machine Learning Research, 2009.
2. D. M. Blei and J. D. Lafferty. Dynamic topic models. Proceedings of the 23rd International Conference on Machine Learning, pages 113-120, 2006.
3. D. M. Blei and J. D. Lafferty. A correlated topic model of science. The Annals of Applied Statistics, 1(1):17-35, 2007.
4. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent Dirichlet allocation. Journal of Machine Learning Research, 3:993-1022, 2003.
5. E.J. Candès and T. Tao. The power of convex relaxation: Near-optimal matrix completion. IEEE Transactions on Information Theory, 56(5):2053-2080, 2010.
6. J. Cinton, S. Jackman, and D. Rivers. The statistical analysis of roll call data. Am. Political Sc. Review, 2004.
7. T. S. Ferguson. A bayesian analysis of some nonparametric problems. The Annals of Statistics, 1(2):209-230, 1973.
8. P. D. Hoff. Multiplicative latent factor models for description and prediction of social networks. Computational and Mathematical Organization Theory, 2009.
9. J. Ishwaran and L. James. Gibbs sampling methods for stick-breaking priors. Journal of the American Statistical Association, 96:161174, 2001.
10. E. Meeds, Z. Ghahramani, R. Neal, and S. Roweis. Modeling dyadic data with binary latent factors. In Advances in NIPS, pages 977-984, 2007.
11. I. Pruteanu-Malinici, L. Ren, J. Paisley, E.Wang, and L. Carin. Hierarchical bayesian modeling of topics in time-stamped documents. IEEE Trans. Pattern Analysis Mach. Intell., 2010.
12. R. Salakhutdinov and A. Mnih. Bayesian probabilistic matrix factorization with mcmc. In Advances in NIPS, 2008.
13. R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. In Advances in NIPS, 2008.
14. J. Sethuraman. A constructive definition of dirichlet priors. Statistica Sinica, 4:639-650, 1994.
15. N. Srebro, J.D.M. Rennie, and T.S. Jaakkola. Maximum-margin matrix factorization. In Advances in NIPS, 2005.
16. R. Thibaux and M.I. Jordan. Hierarchical beta processes and the indian buffet process. In International Conference on Artificial Intelligence and Statistics, 2007.
17. H. M. Wallach. Topic modeling: beyond bag of words. Proceedings of the 23rd International Conference on Machine Learning, 2006.
18. K. Yu, J. Lafferty, S. Zhu, and Y. Gong. Large-scale collaborative prediction using a nonparametric random effects model. In Proc. Int. Conf. Machine Learning, 2009.