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  4. A Graph Diffusion LMS Strategy for Adaptive Graph Signal Processing
 
conference paper

A Graph Diffusion LMS Strategy for Adaptive Graph Signal Processing

Nassif, Roula  
•
Richard, Cédric
•
Chen, Jie
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2017
2017 51st Asilomar Conference on Signals, Systems, and Computers
Asilomar Conference on Signals, Systems, and Computers

The massive deployment of distributed acquisition and signal processing systems, as well as the ubiquity of connected devices, is currently contributing to the development of graph signal processing. Nevertheless, this discipline still suffers from the lack of several theories and methods widely developed in signal processing. In particular, current research activities focus mainly on the processing of static graph signals (with respect to time) despite the natural anchoring in dynamic application contexts. We note, in fact, the lack of works on (on-line) identification of systems operating on streaming graph signals, and on adaptive algorithms to adapt to changes in their statistical properties over time. The objective of this paper is to introduce new tools for adaptive graph signal processing. In the first part, we propose an adaptive filtering method for streaming graph signals based on the LMS. Since this algorithm is centralized, we show how to distribute it across the graph nodes using diffusion adaptation. In the second part, we analyze the performance of the diffusion graph-LMS in both the mean and mean-square sense, as well as its stability

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Type
conference paper
DOI
10.1109/ACSSC.2017.8335711
Author(s)
Nassif, Roula  
Richard, Cédric
Chen, Jie
Sayed, Ali H.  
Date Issued

2017

Published in
2017 51st Asilomar Conference on Signals, Systems, and Computers
Start page

1973

End page

1976,

Editorial or Peer reviewed

REVIEWED

Written at

OTHER

EPFL units
ASL  
Event nameEvent placeEvent date
Asilomar Conference on Signals, Systems, and Computers

Pacific Grove, CA, USA

October 29 - November 1, 2017

Available on Infoscience
December 22, 2017
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/143542
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