Mixture density estimation via hilbert space embedding of measures

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6 Scopus citations

Abstract

In this paper, we consider the problem of estimating a density using a finite combination of densities from a given class, C. Unlike previous works, where Kullback-Leibler (KL) divergence is used as a notion of distance, in this paper, we consider a distance measure based on the embedding of densities into a reproducing kernel Hilbert space (RKHS). We analyze the estimation and approximation errors for an M-estimator and show the estimation error rate to be better than that obtained with KL divergence while achieving the same approximation error rate. Another advantage of the Hilbert space embedding approach is that these results are achieved without making any assumptions on C, in contrast to the KL divergence approach, where the densities in C are assumed to be bounded (and away from zero) with C having a finite Dudley entropy integral.

Original languageEnglish (US)
Title of host publication2011 IEEE International Symposium on Information Theory Proceedings, ISIT 2011
Pages1027-1030
Number of pages4
DOIs
StatePublished - 2011
Event2011 IEEE International Symposium on Information Theory Proceedings, ISIT 2011 - St. Petersburg, Russian Federation
Duration: Jul 31 2011Aug 5 2011

Other

Other2011 IEEE International Symposium on Information Theory Proceedings, ISIT 2011
CountryRussian Federation
CitySt. Petersburg
Period7/31/118/5/11

All Science Journal Classification (ASJC) codes

  • Applied Mathematics
  • Modeling and Simulation
  • Theoretical Computer Science
  • Information Systems

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    Sriperumbudur, B. K. (2011). Mixture density estimation via hilbert space embedding of measures. In 2011 IEEE International Symposium on Information Theory Proceedings, ISIT 2011 (pp. 1027-1030). [6033685] https://doi.org/10.1109/ISIT.2011.6033685