218559
20180317092109.0
978-1-5090-1929-8
000392266500036
ISI
CONF
Source Localization on Graphs via l1 Recovery and Spectral Graph Theory
New York
2016
Ieee
2016
5
Conference Papers
We cast the problem of source localization on graphs as the simultaneous problem of sparse recovery and diffusion ker- nel learning. An l1 regularization term enforces the sparsity constraint while we recover the sources of diffusion from a single snapshot of the diffusion process. The diffusion ker- nel is estimated by assuming the process to be as generic as the standard heat diffusion. We show with synthetic data that we can concomitantly learn the diffusion kernel and the sources, given an estimated initialization. We validate our model with cholera mortality and atmospheric tracer diffusion data, showing also that the accuracy of the solution depends on the construction of the graph from the data points.
source localization
graph
sparsity
optimization
Cerqueira Gonzalez Pena, Rodrigo
254838
249197
Bresson, Xavier
140163
241065
Vandergheynst, Pierre
120906
240428
12th IEEE Image, Video, and Multidimensional Signal Processing (IVMSP) Workshop 2016
Bordeaux, France
July 11-12, 2016
2016 Ieee 12Th Image, Video, And Multidimensional Signal Processing Workshop (Ivmsp)
URL
https://arxiv.org/abs/1603.07584
oai:infoscience.tind.io:218559
STI
conf
LTS2
252392
U10380
254838
EPFL-CONF-218559
EPFL
ACCEPTED
REVIEWED
CONF