Functional epsilon entropy

Sourya Basu, Daewon Seo, Lav Varshney

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We consider the problem of coding for computing with maximal distortion, where the sender communicates with a receiver, which has its own private data and wants to compute a function of their combined data with some fidelity constraint known to both agents. We show that the minimum rate for this problem is equal to the conditional entropy of a hypergraph and design practical codes for the problem. Further, the minimum rate of this problem may be a discontinuous function of the fidelity constraint. We also consider the case when the exact function is not known to the sender, but some approximate function or a class to which the function belongs is known and provide efficient achievable schemes.

Original languageEnglish (US)
Title of host publicationProceedings - DCC 2020
Subtitle of host publicationData Compression Conference
EditorsAli Bilgin, Michael W. Marcellin, Joan Serra-Sagrista, James A. Storer
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages332-341
Number of pages10
ISBN (Electronic)9781728164571
DOIs
StatePublished - Mar 2020
Event2020 Data Compression Conference, DCC 2020 - Snowbird, United States
Duration: Mar 24 2020Mar 27 2020

Publication series

NameData Compression Conference Proceedings
Volume2020-March
ISSN (Print)1068-0314

Conference

Conference2020 Data Compression Conference, DCC 2020
CountryUnited States
CitySnowbird
Period3/24/203/27/20

ASJC Scopus subject areas

  • Computer Networks and Communications

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  • Cite this

    Basu, S., Seo, D., & Varshney, L. (2020). Functional epsilon entropy. In A. Bilgin, M. W. Marcellin, J. Serra-Sagrista, & J. A. Storer (Eds.), Proceedings - DCC 2020: Data Compression Conference (pp. 332-341). [9105781] (Data Compression Conference Proceedings; Vol. 2020-March). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DCC47342.2020.00041