000256648 001__ 256648
000256648 005__ 20190808160227.0
000256648 0247_ $$a10.1109/JPROC.2018.2820126$$2doi
000256648 02470 $$2ArXiv$$a1712.00468
000256648 02470 $$a000433349100004$$2isi
000256648 037__ $$aARTICLE
000256648 245__ $$aGraph Signal Processing: Overview, Challenges and Applications
000256648 260__ $$c2018
000256648 269__ $$a2018
000256648 336__ $$aJournal Articles
000256648 520__ $$aResearch in graph signal processing (GSP) aims to develop tools for processing data defined on irregular graph domains. In this paper, we first provide an overview of core ideas in GSP and their connection to conventional digital signal processing, along with a brief historical perspective to highlight how concepts recently developed in GSP build on top of prior research in other areas. We then summarize recent advances in developing basic GSP tools, including methods for sampling, filtering, or graph learning. Next, we review progress in several application areas using GSP, including processing and analysis of sensor network data, biological data, and applications to image processing and machine learning.
000256648 700__ $$g111295$$aOrtega, Antonio$$0244022
000256648 700__ $$g101475$$aFrossard, Pascal$$0241061
000256648 700__ $$g137506$$aKovacevic, Jelena$$0244024
000256648 700__ $$aMoura, Jose M. F.
000256648 700__ $$0240428$$aVandergheynst, Pierre$$g120906
000256648 773__ $$q808-828$$k5$$j106$$tProceedings of the IEEE
000256648 8560_ $$fpierre.vandergheynst@epfl.ch
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000256648 960__ $$apascal.frossard@epfl.ch
000256648 961__ $$apierre.devaud@epfl.ch
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