A nearest-neighbor approach to estimating divergence between continuous random vectors

Qing Wang; Sanjeev R. Kulkarni; Sergio Verdú

doi:10.1109/ISIT.2006.261842

A nearest-neighbor approach to estimating divergence between continuous random vectors

Qing Wang, Sanjeev R. Kulkarni, Sergio Verdú

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

70 Scopus citations

Abstract

A method for divergence estimation between multidimensional distributions based on nearest neighbor distances is proposed. Given i.i.d. samples, both the bias and the variance of this estimator are proven to vanish as sample sizes go to infinity. In experiments on high-dimensional data, the nearest neighbor approach generally exhibits faster convergence compared to previous algorithms based on partitioning.

Original language	English (US)
Title of host publication	Proceedings - 2006 IEEE International Symposium on Information Theory, ISIT 2006
Pages	242-246
Number of pages	5
DOIs	https://doi.org/10.1109/ISIT.2006.261842
State	Published - 2006
Event	2006 IEEE International Symposium on Information Theory, ISIT 2006 - Seattle, WA, United States Duration: Jul 9 2006 → Jul 14 2006

Publication series

Name	IEEE International Symposium on Information Theory - Proceedings
ISSN (Print)	2157-8101

Other

Other	2006 IEEE International Symposium on Information Theory, ISIT 2006
Country/Territory	United States
City	Seattle, WA
Period	7/9/06 → 7/14/06

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Information Systems
Modeling and Simulation
Applied Mathematics

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10.1109/ISIT.2006.261842

Cite this

@inproceedings{80d3fffc067b4ed58a3b1a7be16dc41f,

title = "A nearest-neighbor approach to estimating divergence between continuous random vectors",

abstract = "A method for divergence estimation between multidimensional distributions based on nearest neighbor distances is proposed. Given i.i.d. samples, both the bias and the variance of this estimator are proven to vanish as sample sizes go to infinity. In experiments on high-dimensional data, the nearest neighbor approach generally exhibits faster convergence compared to previous algorithms based on partitioning.",

author = "Qing Wang and Kulkarni, {Sanjeev R.} and Sergio Verd{\'u}",

year = "2006",

doi = "10.1109/ISIT.2006.261842",

language = "English (US)",

isbn = "1424405041",

series = "IEEE International Symposium on Information Theory - Proceedings",

pages = "242--246",

booktitle = "Proceedings - 2006 IEEE International Symposium on Information Theory, ISIT 2006",

note = "2006 IEEE International Symposium on Information Theory, ISIT 2006 ; Conference date: 09-07-2006 Through 14-07-2006",

}

Wang, Q, Kulkarni, SR & Verdú, S 2006, A nearest-neighbor approach to estimating divergence between continuous random vectors. in Proceedings - 2006 IEEE International Symposium on Information Theory, ISIT 2006., 4035959, IEEE International Symposium on Information Theory - Proceedings, pp. 242-246, 2006 IEEE International Symposium on Information Theory, ISIT 2006, Seattle, WA, United States, 7/9/06. https://doi.org/10.1109/ISIT.2006.261842

A nearest-neighbor approach to estimating divergence between continuous random vectors. / Wang, Qing; Kulkarni, Sanjeev R.; Verdú, Sergio.
Proceedings - 2006 IEEE International Symposium on Information Theory, ISIT 2006. 2006. p. 242-246 4035959 (IEEE International Symposium on Information Theory - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Kulkarni, Sanjeev R.

AU - Verdú, Sergio

PY - 2006

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AB - A method for divergence estimation between multidimensional distributions based on nearest neighbor distances is proposed. Given i.i.d. samples, both the bias and the variance of this estimator are proven to vanish as sample sizes go to infinity. In experiments on high-dimensional data, the nearest neighbor approach generally exhibits faster convergence compared to previous algorithms based on partitioning.

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M3 - Conference contribution

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SN - 9781424405046

T3 - IEEE International Symposium on Information Theory - Proceedings

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BT - Proceedings - 2006 IEEE International Symposium on Information Theory, ISIT 2006

T2 - 2006 IEEE International Symposium on Information Theory, ISIT 2006

Y2 - 9 July 2006 through 14 July 2006

ER -

A nearest-neighbor approach to estimating divergence between continuous random vectors

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