000218893 001__ 218893
000218893 005__ 20190317000454.0
000218893 037__ $$aCONF
000218893 245__ $$aTracking Time-Vertex Propagation using Dynamic Graph Wavelets
000218893 269__ $$a2016
000218893 260__ $$c2016
000218893 336__ $$aConference Papers
000218893 520__ $$aGraph 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.
000218893 6531_ $$aGraph signal processing
000218893 6531_ $$aTime-vertex signal processing
000218893 6531_ $$aConvex optimization
000218893 6531_ $$aDynamic processes on graphs
000218893 6531_ $$aWave equation
000218893 700__ $$0(EPFLAUTH)264158$$g264158$$aGrassi, Francesco
000218893 700__ $$0247306$$g179669$$aPerraudin, Nathanaël
000218893 700__ $$aRicaud, Benjamin$$g229699$$0246772
000218893 7112_ $$dDecember 7–9, 2016$$cWashington D.C., USA$$a4th IEEE Global Conference on Signal and Information Processing
000218893 773__ $$tProceedings of the 4th IEEE Global Conference on Signal and Information Processing
000218893 8564_ $$uhttps://infoscience.epfl.ch/record/218893/files/main.pdf$$zPreprint$$s645422$$yPreprint
000218893 909C0 $$xU10380$$0252392$$pLTS2
000218893 909CO $$qGLOBAL_SET$$pconf$$ooai:infoscience.tind.io:218893$$pSTI
000218893 917Z8 $$x264158
000218893 937__ $$aEPFL-CONF-218893
000218893 973__ $$rREVIEWED$$sSUBMITTED$$aEPFL
000218893 980__ $$aCONF