Mixture density estimation via hilbert space embedding of measures

IEEE International Symposium on Information Theory - Proceedings

Volumn , Issue , 2011, Pages 1027-1030

  Sriperumbudur, Bharath K ^a  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

A-DENSITY; APPROXIMATION ERRORS; DISTANCE MEASURE; ESTIMATION ERRORS; HILBERT; KL-DIVERGENCE; KULLBACK-LEIBLER DIVERGENCE; M-ESTIMATORS; MIXTURE DENSITY; NOTION OF DISTANCE; REPRODUCING KERNEL HILBERT SPACES; SPACE EMBEDDING;

HILBERT SPACES; INFORMATION THEORY;

ESTIMATION;

EID: 80054799691     PISSN: 21578104     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISIT.2011.6033685     Document Type: Conference Paper

References (13)

1. J. Li and A. Barron, "Mixture density estimation", in Advances in Neural Information Processing Systems 12, S. A. Solla, T. K. Leon, and K.-R. Muller, Eds. San Mateo, CA: Morgan Kaufmann Publishers, 1999, pp. 279-285.
2. J. Li, "Estimation of mixture models", Ph. D. dissertation, Department of Statistics, Yale University, 1999.
3. A. Rakhlin, D. Panchenko, and S. Mukherjee, "Risk bounds for mixture density estimation", ESAIM: Probability and Statistics, vol. 9, pp. 220-229, 2005.
4. S. van de Geer, "Rates of convergence for the maximum likelihood estimator in mixture models", Nonparametric Statistics, vol. 6, pp. 293-310, 1996.
5. N. Aronszajn, "Theory of reproducing kernels", Trans. Amer. Math. Soc., vol. 68, pp. 337-404, 1950.
6. A. Berlinet and C. Thomas-Agnan, Reproducing Kernel Hilbert Spaces in Probability and Statistics. London, UK: Kluwer Academic Publishers, 2004.
7. A. J. Smola, A. Gretton, L. Song, and B. Schölkopf, "A Hilbert space embedding for distributions", in Proc. 18th International Conference on Algorithmic Learning Theory. Springer-Verlag, Berlin, Germany, 2007, pp. 13-31.
8. B. K. Sriperumbudur, A. Gretton, K. Fukumizu, B. Schölkopf, and G. R. G. Lanckriet, "Hilbert space embeddings and metrics on probability measures", Journal of Machine Learning Research, vol. 11, pp. 1517-1561, 2010.
9. K. Fukumizu, A. Gretton, X. Sun, and B. Schölkopf, "Kernel measures of conditional dependence", in Advances in Neural Information Processing Systems 20, J. Platt, D. Koller, Y. Singer, and S. Roweis, Eds. Cambridge, MA: MIT Press, 2008, pp. 489-496.
10. K. Fukumizu, B. K. Sriperumbudur, A. Gretton, and B. Schölkopf, "Characteristic kernels on groups and semigroups", in Advances in Neural Information Processing Systems 21, D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, Eds., 2009, pp. 473-480.
11. A. Gretton, K. M. Borgwardt, M. Rasch, B. Schölkopf, and A. Smola, "A kernel method for the two sample problem", in Advances in Neural Information Processing Systems 19, B. Schölkopf, J. Platt, and T. Hoffman, Eds. MIT Press, 2007, pp. 513-520.
12. A. W. van der Vaart and J. A. Wellner, Weak Convergence and Empirical Processes. New York: Springer-Verlag, 1996.
13. I. Steinwart and A. Christmann, Support Vector Machines. Springer, 2008.