000261426 001__ 261426 000261426 005__ 20190121181806.0 000261426 022__ $$a1687-6180 000261426 02470 $$2isi$$a000448552000001 000261426 0247_ $$a10.1186/s13634-018-0584-2$$2doi 000261426 037__ $$aARTICLE 000261426 245__ $$aLearning of robust spectral graph dictionaries for distributed processing 000261426 260__ $$bSPRINGEROPEN$$aLondon$$c2018 000261426 269__ $$a2018-10-24 000261426 336__ $$aJournal Articles 000261426 520__ $$aWe consider the problem of distributed representation of signals in sensor networks, where sensors exchange quantized information with their neighbors. The signals of interest are assumed to have a sparse representation with spectral graph dictionaries. We further model the spectral dictionaries as polynomials of the graph Laplacian operator. We first study the impact of the quantization noise in the distributed computation of matrix-vector multiplications, such as the forward and the adjoint operator, which are used in many classical signal processing tasks. It occurs that the performance is clearly penalized by the quantization noise, whose impact directly depends on the structure of the spectral graph dictionary. Next, we focus on the problem of sparse signal representation and propose an algorithm to learn polynomial graph dictionaries that are both adapted to the graph signals of interest and robust to quantization noise. Simulation results show that the learned dictionaries are efficient in processing graph signals in sensor networks where bandwidth constraints impose quantization of the messages exchanged in the network. 000261426 650__ $$aEngineering, Electrical & Electronic 000261426 650__ $$aEngineering 000261426 6531_ $$adistributed processing 000261426 6531_ $$agraph signal processing 000261426 6531_ $$aquantization 000261426 6531_ $$apolynomial dictionaries 000261426 6531_ $$asparse approximation 000261426 6531_ $$asignals 000261426 700__ $$0244101$$aThanou, Dorina 000261426 700__ $$0241061$$aFrossard, Pascal 000261426 773__ $$q67$$tEurasip Journal On Advances In Signal Processing 000261426 8560_ $$fpascal.frossard@epfl.ch 000261426 909C0 $$yApproved$$pLTS4$$xU10851$$mpascal.frossard@epfl.ch$$zMarselli, Béatrice$$0252393 000261426 909CO $$ooai:infoscience.epfl.ch:261426$$particle$$pSTI 000261426 961__ $$afantin.reichler@epfl.ch 000261426 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL 000261426 980__ $$aARTICLE 000261426 980__ $$aWoS 000261426 981__ $$aoverwrite