218893
20190317000454.0
CONF
Tracking Time-Vertex Propagation using Dynamic Graph Wavelets
2016
2016
Conference Papers
Graph Signal Processing generalizes classical signal processing to signal or data indexed by the vertices of a weighted graph. So far, the research efforts have been focused on static graph signals. However numerous applications involve graph signals evolving in time, such as spreading or propagation of waves on a network. The analysis of this type of data requires a new set of methods that fully takes into account the time and graph dimensions. We propose a novel class of wavelet frames named Dynamic Graph Wavelets, whose time-vertex evolution follows a dynamic process. We demonstrate that this set of functions can be combined with sparsity based approaches such as compressive sensing to reveal information on the dynamic processes occurring on a graph. Experiments on real seismological data show the efficiency of the technique, allowing to estimate the epicenter of earthquake events recorded by a seismic network.
Graph signal processing
Time-vertex signal processing
Convex optimization
Dynamic processes on graphs
Wave equation
(EPFLAUTH)264158
Grassi, Francesco
264158
247306
Perraudin, Nathanaël
179669
246772
Ricaud, Benjamin
229699
4th IEEE Global Conference on Signal and Information Processing
Washington D.C., USA
December 7–9, 2016
Proceedings of the 4th IEEE Global Conference on Signal and Information Processing
645422
http://infoscience.epfl.ch/record/218893/files/main.pdf
Preprint
Preprint
252392
LTS2
U10380
oai:infoscience.tind.io:218893
conf
STI
GLOBAL_SET
264158
EPFL-CONF-218893
EPFL
REVIEWED
SUBMITTED
CONF