TY - GEN
T1 - Distributed Diffusion Adaptation over Graph Signals
AU - Nassif, Roula
AU - Richard, Cedric
AU - Chen, Jie
AU - Sayed, Ali H.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/10
Y1 - 2018/9/10
N2 - 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.
AB - 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.
KW - Diffusion strategies
KW - Graph filtering
KW - Graph signal processing
KW - Streaming graph signals
UR - http://www.scopus.com/inward/record.url?scp=85054288460&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2018.8461574
DO - 10.1109/ICASSP.2018.8461574
M3 - 会议稿件
AN - SCOPUS:85054288460
SN - 9781538646588
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 4129
EP - 4133
BT - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Y2 - 15 April 2018 through 20 April 2018
ER -