Universal estimation of entropy and divergence via block sorting

Haixiao Cai; Sanjeev R. Kulkarni; Sergio Verdú

Universal estimation of entropy and divergence via block sorting

Haixiao Cai, Sanjeev R. Kulkarni, Sergio Verdú

Research output: Contribution to journal › Conference article › peer-review

Abstract

A new algorithm to estimate both entropy and divergence of two finite-alphabet, finite memory tree sources, using information provided by a realization from each of the two sources was presented. The algorithm outperforms a previous LZ-based method. It is motivated by data compression based on the Burrows-Wheeler Block Sorting Transform.

Original language	English (US)
Pages (from-to)	433
Number of pages	1
Journal	IEEE International Symposium on Information Theory - Proceedings
State	Published - 2002
Event	2002 IEEE International Symposium on Information Theory - Lausanne, Switzerland Duration: Jun 30 2002 → Jul 5 2002

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Information Systems
Modeling and Simulation
Applied Mathematics

Cite this

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title = "Universal estimation of entropy and divergence via block sorting",

abstract = "A new algorithm to estimate both entropy and divergence of two finite-alphabet, finite memory tree sources, using information provided by a realization from each of the two sources was presented. The algorithm outperforms a previous LZ-based method. It is motivated by data compression based on the Burrows-Wheeler Block Sorting Transform.",

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

year = "2002",

language = "English (US)",

pages = "433",

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

issn = "2157-8095",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "2002 IEEE International Symposium on Information Theory ; Conference date: 30-06-2002 Through 05-07-2002",

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TY - JOUR

T1 - Universal estimation of entropy and divergence via block sorting

AU - Cai, Haixiao

AU - Kulkarni, Sanjeev R.

AU - Verdú, Sergio

PY - 2002

Y1 - 2002

N2 - A new algorithm to estimate both entropy and divergence of two finite-alphabet, finite memory tree sources, using information provided by a realization from each of the two sources was presented. The algorithm outperforms a previous LZ-based method. It is motivated by data compression based on the Burrows-Wheeler Block Sorting Transform.

AB - A new algorithm to estimate both entropy and divergence of two finite-alphabet, finite memory tree sources, using information provided by a realization from each of the two sources was presented. The algorithm outperforms a previous LZ-based method. It is motivated by data compression based on the Burrows-Wheeler Block Sorting Transform.

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UR - http://www.scopus.com/inward/citedby.url?scp=0036351999&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:0036351999

SN - 2157-8095

SP - 433

JO - IEEE International Symposium on Information Theory - Proceedings

JF - IEEE International Symposium on Information Theory - Proceedings

T2 - 2002 IEEE International Symposium on Information Theory

Y2 - 30 June 2002 through 5 July 2002

ER -

Universal estimation of entropy and divergence via block sorting

Abstract

All Science Journal Classification (ASJC) codes

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