A graph diffusion LMS strategy for adaptive graph signal processing

Roula Nassif; Cedric Richard; Jie Chen; Ali H. Sayed

doi:10.1109/ACSSC.2017.8335711

A graph diffusion LMS strategy for adaptive graph signal processing

Roula Nassif, Cedric Richard, Jie Chen, Ali H. Sayed

School of Marine Science and Technology

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

15 Scopus citations

Abstract

Graph signal processing allows the generalization of DSP concepts to the graph domain. However, most works assume graph signals that are static with respect to time, which is a limitation even in comparison to classical DSP formulations where signals are generally sequences that evolve over time. Several earlier works on adaptive networks have addressed problems involving streaming data over graphs by developing effective learning strategies that are well-suited to dynamic data scenarios, in a manner that generalizes adaptive signal processing concepts to the graph domain. The objective of this paper is to blend concepts from adaptive networks and graph signal processing to propose new useful tools for adaptive graph signal processing.

Original language	English
Title of host publication	Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
Editors	Michael B. Matthews
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1973-1976
Number of pages	4
ISBN (Electronic)	9781538618233
DOIs	https://doi.org/10.1109/ACSSC.2017.8335711
State	Published - 2 Jul 2017
Event	51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017 - Pacific Grove, United States Duration: 29 Oct 2017 → 1 Nov 2017

Publication series

Name	Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
Volume	2017-October

Conference

Conference	51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
Country/Territory	United States
City	Pacific Grove
Period	29/10/17 → 1/11/17

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10.1109/ACSSC.2017.8335711

Cite this

Nassif, R., Richard, C., Chen, J., & Sayed, A. H. (2017). A graph diffusion LMS strategy for adaptive graph signal processing. In M. B. Matthews (Ed.), Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017 (pp. 1973-1976). (Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017; Vol. 2017-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACSSC.2017.8335711

Nassif, Roula ; Richard, Cedric ; Chen, Jie et al. / A graph diffusion LMS strategy for adaptive graph signal processing. Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017. editor / Michael B. Matthews. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1973-1976 (Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017).

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abstract = "Graph signal processing allows the generalization of DSP concepts to the graph domain. However, most works assume graph signals that are static with respect to time, which is a limitation even in comparison to classical DSP formulations where signals are generally sequences that evolve over time. Several earlier works on adaptive networks have addressed problems involving streaming data over graphs by developing effective learning strategies that are well-suited to dynamic data scenarios, in a manner that generalizes adaptive signal processing concepts to the graph domain. The objective of this paper is to blend concepts from adaptive networks and graph signal processing to propose new useful tools for adaptive graph signal processing.",

author = "Roula Nassif and Cedric Richard and Jie Chen and Sayed, {Ali H.}",

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Nassif, R, Richard, C, Chen, J & Sayed, AH 2017, A graph diffusion LMS strategy for adaptive graph signal processing. in MB Matthews (ed.), Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017. Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017, vol. 2017-October, Institute of Electrical and Electronics Engineers Inc., pp. 1973-1976, 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017, Pacific Grove, United States, 29/10/17. https://doi.org/10.1109/ACSSC.2017.8335711

A graph diffusion LMS strategy for adaptive graph signal processing. / Nassif, Roula; Richard, Cedric; Chen, Jie et al.
Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017. ed. / Michael B. Matthews. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1973-1976 (Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017; Vol. 2017-October).

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

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Nassif R, Richard C, Chen J, Sayed AH. A graph diffusion LMS strategy for adaptive graph signal processing. In Matthews MB, editor, Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1973-1976. (Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017). doi: 10.1109/ACSSC.2017.8335711

A graph diffusion LMS strategy for adaptive graph signal processing

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