Functional epsilon entropy

Sourya Basu; Daewon Seo; Lav Varshney

doi:10.1109/DCC47342.2020.00041

Functional epsilon entropy

Sourya Basu, Daewon Seo, Lav Varshney

Department of Electrical Engineering and Computer Science

University of Illinois at Urbana-Champaign

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

7 Scopus citations

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 language	English
Title of host publication	Proceedings - DCC 2020
Subtitle of host publication	Data Compression Conference
Editors	Ali Bilgin, Michael W. Marcellin, Joan Serra-Sagrista, James A. Storer
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	332-341
Number of pages	10
ISBN (Electronic)	9781728164571
DOIs	https://doi.org/10.1109/DCC47342.2020.00041
State	Published - Mar 2020
Event	2020 Data Compression Conference, DCC 2020 - Snowbird, United States Duration: 24 Mar 2020 → 27 Mar 2020

Publication series

Name	Data Compression Conference Proceedings
Volume	2020-March
ISSN (Print)	1068-0314

Conference

Conference	2020 Data Compression Conference, DCC 2020
Country/Territory	United States
City	Snowbird
Period	24/03/20 → 27/03/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Access to Document

10.1109/DCC47342.2020.00041

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). Article 9105781 (Data Compression Conference Proceedings; Vol. 2020-March). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DCC47342.2020.00041

@inproceedings{6ede4ca997e24e2b9dcff4c5b08a3fa7,

title = "Functional epsilon entropy",

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.",

author = "Sourya Basu and Daewon Seo and Lav Varshney",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 Data Compression Conference, DCC 2020 ; Conference date: 24-03-2020 Through 27-03-2020",

year = "2020",

month = mar,

doi = "10.1109/DCC47342.2020.00041",

language = "English",

series = "Data Compression Conference Proceedings",

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

pages = "332--341",

editor = "Ali Bilgin and Marcellin, {Michael W.} and Joan Serra-Sagrista and Storer, {James A.}",

booktitle = "Proceedings - DCC 2020",

}

Basu, S, Seo, D & Varshney, L 2020, Functional epsilon entropy. in A Bilgin, MW Marcellin, J Serra-Sagrista & JA Storer (eds), Proceedings - DCC 2020: Data Compression Conference., 9105781, Data Compression Conference Proceedings, vol. 2020-March, Institute of Electrical and Electronics Engineers Inc., pp. 332-341, 2020 Data Compression Conference, DCC 2020, Snowbird, United States, 24/03/20. https://doi.org/10.1109/DCC47342.2020.00041

Functional epsilon entropy. / Basu, Sourya; Seo, Daewon; Varshney, Lav.
Proceedings - DCC 2020: Data Compression Conference. ed. / Ali Bilgin; Michael W. Marcellin; Joan Serra-Sagrista; James A. Storer. Institute of Electrical and Electronics Engineers Inc., 2020. p. 332-341 9105781 (Data Compression Conference Proceedings; Vol. 2020-March).

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

TY - GEN

T1 - Functional epsilon entropy

AU - Basu, Sourya

AU - Seo, Daewon

AU - Varshney, Lav

PY - 2020/3

Y1 - 2020/3

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85086840492&partnerID=8YFLogxK

U2 - 10.1109/DCC47342.2020.00041

DO - 10.1109/DCC47342.2020.00041

M3 - Conference contribution

AN - SCOPUS:85086840492

T3 - Data Compression Conference Proceedings

SP - 332

EP - 341

BT - Proceedings - DCC 2020

A2 - Bilgin, Ali

A2 - Marcellin, Michael W.

A2 - Serra-Sagrista, Joan

A2 - Storer, James A.

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 Data Compression Conference, DCC 2020

Y2 - 24 March 2020 through 27 March 2020

ER -

Functional epsilon entropy

Abstract

Publication series

Conference

Bibliographical note

Access to Document

Other files and links

Fingerprint

Cite this