Distributed Diffusion Adaptation over Graph Signals

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

科研成果: 书/报告/会议事项章节会议稿件同行评审

42 引用 (Scopus)

摘要

Most works on graph signal processing assume static graph signals, which is a limitation even in comparison to traditional DSP techniques where signals are modeled as sequences that evolve over time. For broader applicability, it is necessary to develop techniques that are able to process dynamic or streaming data. Many earlier works on adaptive networks have addressed problems related to this challenge by developing effective strategies that are particularly well-suited to data streaming into graphs. We are thus faced with two paradigms: one where signals are modeled as static and sitting on the graph nodes, and another where signals are modeled as dynamic and streaming into the graph nodes. The objective of this work is to blend these concepts and propose diffusion strategies for adaptively learning from streaming graph signals.

源语言英语
主期刊名2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
出版商Institute of Electrical and Electronics Engineers Inc.
4129-4133
页数5
ISBN(印刷版)9781538646588
DOI
出版状态已出版 - 10 9月 2018
活动2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, 加拿大
期限: 15 4月 201820 4月 2018

出版系列

姓名ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
2018-April
ISSN(印刷版)1520-6149

会议

会议2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
国家/地区加拿大
Calgary
时期15/04/1820/04/18

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