Graph Signal Processing: Overview, Challenges, and Applications

Research 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 highli...

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Vydané v:	Proceedings of the IEEE Ročník 106; číslo 5; s. 808 - 828
Hlavní autori:	Ortega, Antonio, Frossard, Pascal, Kovacevic, Jelena, Moura, Jose M. F., Vandergheynst, Pierre
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.05.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Data models Data processing Digital signal processing Digital signal processors Filtration Graph signal processing (GSP) Graph theory Graphical models Image processing Laplace equations Machine learning network science and graphs sampling Sampling methods Signal processing Social network services
ISSN:	0018-9219, 1558-2256
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Abstract	Research 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.
AbstractList	Research 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.
Author	Ortega, Antonio Moura, Jose M. F. Vandergheynst, Pierre Frossard, Pascal Kovacevic, Jelena
Author_xml	– sequence: 1 givenname: Antonio orcidid: 0000-0001-5403-0940 surname: Ortega fullname: Ortega, Antonio email: antonio.ortega@sipi.usc.edu organization: University of Southern California, Los Angeles, CA, USA – sequence: 2 givenname: Pascal orcidid: 0000-0002-4010-714X surname: Frossard fullname: Frossard, Pascal organization: EPFL, Lausanne, Switzerland-1015, Lausanne – sequence: 3 givenname: Jelena surname: Kovacevic fullname: Kovacevic, Jelena organization: Carnegie Mellon University, Pittsburgh, PA, USA – sequence: 4 givenname: Jose M. F. orcidid: 0000-0002-9822-8294 surname: Moura fullname: Moura, Jose M. F. organization: Carnegie Mellon University, Pittsburgh, PA, USA – sequence: 5 givenname: Pierre surname: Vandergheynst fullname: Vandergheynst, Pierre organization: EPFL, Lausanne, Switzerland-1015, Lausanne
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Snippet	Research in graph signal processing (GSP) aims to develop tools for processing data defined on irregular graph domains. In this paper, we first provide an...
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SubjectTerms	Data models Data processing Digital signal processing Digital signal processors Filtration Graph signal processing (GSP) Graph theory Graphical models Image processing Laplace equations Machine learning network science and graphs sampling Sampling methods Signal processing Social network services
Title	Graph Signal Processing: Overview, Challenges, and Applications
URI	https://ieeexplore.ieee.org/document/8347162 https://www.proquest.com/docview/2031103191
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