261426
20190121181806.0
1687-6180
000448552000001
isi
10.1186/s13634-018-0584-2
doi
ARTICLE
Learning of robust spectral graph dictionaries for distributed processing
2018
London
SPRINGEROPEN
2018-10-24
Journal Articles
We consider the problem of distributed representation of signals in sensor networks, where sensors exchange quantized information with their neighbors. The signals of interest are assumed to have a sparse representation with spectral graph dictionaries. We further model the spectral dictionaries as polynomials of the graph Laplacian operator. We first study the impact of the quantization noise in the distributed computation of matrix-vector multiplications, such as the forward and the adjoint operator, which are used in many classical signal processing tasks. It occurs that the performance is clearly penalized by the quantization noise, whose impact directly depends on the structure of the spectral graph dictionary. Next, we focus on the problem of sparse signal representation and propose an algorithm to learn polynomial graph dictionaries that are both adapted to the graph signals of interest and robust to quantization noise. Simulation results show that the learned dictionaries are efficient in processing graph signals in sensor networks where bandwidth constraints impose quantization of the messages exchanged in the network.
Engineering, Electrical & Electronic
Engineering
distributed processing
graph signal processing
quantization
polynomial dictionaries
sparse approximation
signals
Thanou, Dorina
244101
Frossard, Pascal
241061
67
Eurasip Journal On Advances In Signal Processing
pascal.frossard@epfl.ch
252393
Marselli, BĂ©atrice
pascal.frossard@epfl.ch
U10851
LTS4
Approved
oai:infoscience.epfl.ch:261426
article
STI
fantin.reichler@epfl.ch
EPFL
PUBLISHED
REVIEWED
ARTICLE
WoS
overwrite