Copula mixture model for dependency-seeking clustering

Proceedings of the 29th International Conference on Machine Learning, ICML 2012

Volumn 1, Issue , 2012, Pages 927-934

  Rey, Mélanie ^a   Roth, Volker ^a  

^a UNIVERSITY OF BASEL   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

CANONICAL CORRELATION ANALYSIS; DATA SOURCE; INCREASED FLEXIBILITY; INTERPRETABILITY; MIXTURE MODEL; MULTIVARIATE DATA; NON-PARAMETRIC BAYESIAN; SYNTHETIC AND REAL DATA;

LEARNING SYSTEMS;

MIXTURES;

EID: 84867125131     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Bach, F.R. and Jordan, M.I. A probabilistic interpretation of canonical correlation analysis. Technical report 688, Department of Statistics, University of California, Berkeley, 2005.
2. Barnard, J., McCulloch, R., and Meng, X. Modeling covariance matrices in terms of standard deviations and correlations, with application to shrinkage. Statistic Sinica, 10:1281-1311, 2000.
3. Eden, E., Navon, R., Steinfeld, I., Lipson, D., and Yakhini, Z. GOrilla: a tool for discovery and visualization of enriched go terms in ranked gene lists. BMC Bioinformatics, 2009.
4. Ferguson, T. A Bayesian analysis of some nonparametric problems. Annals of Statistics, l(2):209-230, 1973.
5. Fern, X.Z., Brodley, C.E., and Friedl, M.A. Correlation clustering for learning mixture of canonical correlation models. Accepted for SIAM International Conference on Data Mining, 2005.
6. Gasch, A.P., Spellman, P.T., and C.M. Kao, et al. Genomic expression programs in the response of yeast cells to environmental changes. Molecular Biology of the Cell, 11:4241-4257, 2000.
7. Harbison, C.T., Gordon, D.B., and T.I. Lee, et al. Transcriptional regulatory code of a eukaryotic genome. Nature, 431:99-104, 2004.
8. Hoff, Peter D. Extending the rank likelihood for semi-parametric copula estimation. Annals of Applied Statistics, 1(1):273, 2007.
9. Hohmann, S. and Mager, W. H. Yeast Stress Responses. Topics in Current Genetics, Vol. 1. Springer, 2003.
10. Klami, A. and Kaski, S. Local dependent components. Proceedings of the 24th International Conference on Machine Learning, 2007.
11. Klami, A. and Kaski, S. Probabilistic approach to detecting dependencies between data sets. Neurocomputing, (72):39-46, 2008.
12. Klami, A., Virtanen, S., and Kaski, S. Bayesian exponential family projections for coupled data sources. Uncertainty in Artificial Intelligence, 2010.
13. McNeil, A. J., Frey, R., and Embrechts, P. Quantitative Risk Management. Princeton Series in Finance. Princeton University Press, 2005.
14. Neal, R.M. Markov chain sampling methods for Dirichlet process mixture models. Technical report 9815, Department of Statistics, University of Toronto, 2011.
15. Rasmussen, C. E. and Görür, D. Dirichlet process Gaussian mixture models: Choice of the base distribution. Journal of Computer Science and Technology, 25(4):615-626, 2010.
16. Sklar, A. Fonctions de répartition àn dimensions et leurs marges. Publications de l'Institut de Statistique de l'Université de Paris, 8:229-231, 1959.
17. Viinikanoja, J., Klami, A., and Kaski, S. Variational Bayesian mixture of robust CCA models. Principles of Data Mining and Knowledge Discovery, pp. 370-385, 2010.
18. Vrac, Mathieu. CCMtools: Clustering through "Correlation Clustering Model" (CCM) and cluster analysis tools., 2010. URL http://CRAN.R-project. org/package=CCMtools. R package version 1.0.