Subset infinite relational models

Journal of Machine Learning Research

Volumn 22, Issue , 2012, Pages 547-555

  Ishiguro, Katsuhiko ^a   Ueda, Naonori ^a   Sawada, Hiroshi ^a  

^a Nippon Telegraph and Telephone Corporation   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

STOCHASTIC SYSTEMS;

CONVENTIONAL MODELING; GENERATIVE MODEL; INFINITE RELATIONAL MODELS; LATENT VARIABLE; ONLINE SHOPS; REAL-WORLD; RELATIONAL DATA; SYNTHETIC AND REAL DATA;

ARTIFICIAL INTELLIGENCE;

EID: 84880710954     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (20)

1. D. Blackwell and J. B. MacQueen. Ferguson distributions via Polya urn schemes. The Annals of Statistics, 1 (2): 353-355, 1973.
2. A. Bordes, J. Weston, R. Collobert, and Y. Bengio. Learning structured embeddings of knowledge bases. In Proc. AAAI, 2011.
3. C. M. Carvalho, J. Chang, J. E. Lucas, J. R. Nevins, Q. Wang, and M. West. High-dimensional sparse factor modeling: Applications in gene expression genomics. J. Am. Stat. Assoc., 103 (484): 1438-1456, 2008.
4. A. Clauset, C. Moore, and M. E. J. Newman. Hierarchical structure and the prediction of missing links in networks. Nature, 453: 98-101, 2008.
5. E. Erosheva, S. Fienberg, and J. Lafferty. Mixed- membership models of scientific publications. PNAS, 101 (Suppl 1): 5220-5227, 2004.
6. W. Fu, L. Song, and E. P. Xing. Dynamic mixed member- ship blockmodel for evolving networks. In Proc. ICML, 2009.
7. T. L. Griffiths and Z. Ghahramani. The Indian buffet pro- cess: An introduction and review. Journal of Machine Learning Research, 12: 1185-1224, 2011.
8. Y. Guan, J. Dy, and M. Jordan. A unified probabilistic model for global and local unsupervised feature selec- tion. In Proc. ICML, 2011.
9. P. D. Hoff. Subset clustering of binary sequences, with an application to genomic abnormality data. Biometrics, 61 (4): 1027-1036, 2005.
10. P. D. Hoff. Model-based subspace clustering. Bayesian Analysis, 1 (2): 321-344, 2006.
11. K. Ishiguro, T. Iwata, N. Ueda, and J. Tenenbaum. Dy- namic infinite relational model for time-varying rela- tional data analysis. In Proc. NIPS, 2010.
12. C. Kemp, J. B. Tenenbaum, T. L. Griffiths, T. Yamada, and N. Ueda. Learning systems of concepts with an infinite relational model. In Proc. AAAI, 2006.
13. B. Klimat and Y. Yang. The enron corpus: A new dataset for email classification research. In Proc. ECML, 2004.
14. D. Liben-Nowell and J. Kleinberg. The link prediction problem for social networks. In Proc. Int. Conf. on In- formation and Knowledge Management, pages 556-559, 2003.
15. K. T. Miller, T. L. Griffiths, and M. I. Jordan. Nonpara- metric latent feature models for link prediction. In Proc. NIPS, 2009.
16. K. Nowicki and T. A. B. Snijders. Estimation and predic- tion for stochastic blockstructures. J. Am. Stat. Assoc., 96 (455): 1077-1087, 2001.
17. J. Sethuraman. A constructive definition of dirichlet pro- cess. Statistica Sinica, 4: 639-650, 1994.
18. I. Sutskever, R. Salakhutdinov, and J. B. Tenenbaum. Mod- elling relational data using Bayesian clustered tensor fac- torization. In Proc. NIPS, 2010.
19. L. Tang, H. Liu, J. Zhang, and Z. Nazeri. Community evolution in dynamic multi-mode networks. In Proc. SIGKDD, pages 677-685, 2008.
20. S. Zhu, K. Yu, and Y. Gong. Stochastic relational models for large-scale dyadic data using mcmc. In Proc. NIPS, 2009.