Binary to bushy: Bayesian hierarchical clustering with the beta coalescent

Advances in Neural Information Processing Systems

Volumn , Issue , 2013, Pages

  Hu, Yuening ^a   Boyd Graber, Jordan ^a   Daumè III, Hal ^a   Ying, Z Irene ^b  

^a UNIVERSITY OF MARYLAND   (United States)

^b AGRICULTURAL RESEARCH SERVICE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BELIEF PROPAGATION; COMBINATORIAL SEARCH; DIRICHLET PROCESS MIXTURE MODEL; HIER-ARCHICAL CLUSTERING; PROBABILISTIC MODELING; SAMPLING SCHEMES; SEQUENTIAL MONTE CARLO; SYNTHETIC AND REAL DATA;

TREES (MATHEMATICS);

EID: 84899012027     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (35)

1. Kaufman, L., P. Rousseeuw. Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley, 1990.
2. Jain, A. K. Data clustering: 50 years beyond k-means. Pattern Recognition Letters, 31(8):651-666, 2010.
3. Brown, P. F., V. J. D. Pietra, P. V. deSouza, et al. Class-based n-gram models of natural language. Computational Linguistics, 18:18-4, 1990.
4. Bergen, J., P. Anandan, K. Hanna, et al. Hierarchical model-based motion estimation. In ECCV. 1992.
5. Girvan, M., M. E. J. Newman. Community structure in social and biological networks. PNAS, 99:7821-7826, 2002.
6. Kingman, J. F. C. On the genealogy of large populations. Journal of Applied Probability, 19:27-43, 1982.
7. Pitman, J. Coalescents with multiple collisions. The Annals of Probability, 27:1870-1902, 1999.
8. Berestycki, N. Recent progress in coalescent theory. In Ensaios Matematicos, vol. 16. 2009.
9. Teh, Y. W., H. Daumé III, D. M. Roy. Bayesian agglomerative clustering with coalescents. In NIPS. 2008.
10. Heller, K. A., Z. Ghahramani. Bayesian hierarchical clustering. In ICML. 2005.
11. Blundell, C., Y. W. Teh, K. A. Heller. Bayesian rose trees. In UAI. 2010.
12. Adams, R., Z. Ghahramani, M. Jordan. Tree-structured stick breaking for hierarchical data. In NIPS. 2010.
13. Knowles, D., Z. Ghahramani. Pitman-Yor diffusion trees. In UAI. 2011.
14. Neal, R. M. Density modeling and clustering using Dirichlet diffusion trees. Bayesian Statistics, 7:619-629, 2003.
15. Sagitov, S. The general coalescent with asynchronous mergers of ancestral lines. Journal of Applied Probability, 36:1116-1125, 1999.
16. Neal, R. M. Annealed importance sampling. Technical report 9805, University of Toronto, 1998.
17. Fearhhead, P. Sequential Monte Carlo method in filter theory. PhD thesis, University of Oxford, 1998.
18. Felsenstein, J. Maximum-likelihood estimation of evolutionary trees from continuous characters. Am J Hum Genet, 25(5):471-492, 1973.
19. Birkner, M., J. Blath, M. Steinrucken. Importance sampling for lambda-coalescents in the infinitely many sites model. Theoretical population biology, 79(4):155-73, 2011.
20. Doucet, A., N. De Freitas, N. Gordon, eds. Sequential Monte Carlo methods in practice. 2001.
21. Gordon, N., D. Salmond, A. Smith. Novel approach to nonlinear/non- Gaussian Bayesian state estimation. IEEE Proceedings F, Radar and Signal Processing, 140(2):107-113, 1993.
22. Görür, D., L. Boyles, M. Welling. Scalable inference on Kingman's coalescent using pair similarity. JMLR, 22:440-448, 2012.
23. Antoniak, C. E. Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems. The Annals of Statistics, 2(6):1152-1174, 1974.
24. Cappe, O., S. Godsill, E. Moulines. An overview of existing methods and recent advances in sequential Monte Carlo. PROCEEDINGS-IEEE, 95(5):899, 2007.
25. Chen, Z. Bayesian filtering: From kalman filters to particle filters, and beyond. McMaster, [Online], 2003.
26. Eads, D. Hierarchical clustering (scipy.cluster.hierarchy). SciPy, 2007.
27. Neal, R. M. Slice sampling. Annals of Statistics, 31:705-767, 2003.
28. Powers, D. M. W. Unsupervised learning of linguistic structure an empirical evaluation. International Journal of Corpus Linguistics, 2:91-131, 1997.
29. Jongeneel, C., M. Delorenzi, C. Iseli, et al. An atlas of human gene expression from massively parallel signature sequencing (mpss). Genome Res, 15:1007-1014, 2005.
30. Shlens, J. A tutorial on principal component analysis. In Systems Neurobiology Laboratory, Salk Institute for Biological Studies. 2005.
31. Blei, D. M., A. Ng, M. Jordan. Latent Dirichlet al.location. JMLR, 2003.
32. Rai, P., H. Daumé III. The infinite hierarchical factor regression model. In NIPS. 2008.
33. Koo, T., X. Carreras, M. Collins. Simple semi-supervised dependency parsing. In ACL. 2008.
34. Boyd-Graber, J., P. Resnik. Holistic sentiment analysis across languages: Multilingual supervised latent Dirichlet al.location. In EMNLP. 2010.
35. Andrzejewski, D., X. Zhu, M. Craven. Incorporating domain knowledge into topic modeling via Dirichlet forest priors. In ICML. 2009.