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

航海学院

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

15 引用（Scopus）

摘要

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.

源语言	英语
主期刊名	Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
编辑	Michael B. Matthews
出版商	Institute of Electrical and Electronics Engineers Inc.
页	1973-1976
页数	4
ISBN（电子版）	9781538618233
DOI	https://doi.org/10.1109/ACSSC.2017.8335711
出版状态	已出版 - 2 7月 2017
活动	51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017 - Pacific Grove, 美国期限: 29 10月 2017 → 1 11月 2017

出版系列

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

会议

会议	51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
国家/地区	美国
市	Pacific Grove
时期	29/10/17 → 1/11/17

访问文件

10.1109/ACSSC.2017.8335711

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链接到 Scopus 的出版物

引用此

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

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

@inproceedings{f38540e35c05447dae3c0d4b1181e06b,

title = "A graph diffusion LMS strategy for adaptive graph signal processing",

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.}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017 ; Conference date: 29-10-2017 Through 01-11-2017",

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Nassif, R, Richard, C, Chen, J & Sayed, AH 2017, A graph diffusion LMS strategy for adaptive graph signal processing. 在 MB Matthews (编辑), 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, 卷 2017-October, Institute of Electrical and Electronics Engineers Inc., 页码 1973-1976, 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017, Pacific Grove, 美国, 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 等.
Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017. 编辑 / Michael B. Matthews. Institute of Electrical and Electronics Engineers Inc., 2017. 页码 1973-1976 (Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017; 卷 2017-October).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

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AU - Sayed, Ali H.

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N2 - 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.

AB - 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.

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BT - Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017

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Nassif R, Richard C, Chen J, Sayed AH. A graph diffusion LMS strategy for adaptive graph signal processing. 在 Matthews MB, 编辑, Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017. Institute of Electrical and Electronics Engineers Inc. 2017. 页码 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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