@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",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/ACSSC.2017.8335711",
language = "英语",
series = "Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1973--1976",
editor = "Matthews, \{Michael B.\}",
booktitle = "Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017",
}