TY - JOUR
T1 - Graph Signal Processing
T2 - History, development, impact, and outlook
AU - Leus, Geert
AU - Marques, Antonio G.
AU - Moura, Jose M.F.
AU - Ortega, Antonio
AU - Shuman, David I.
N1 - Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
PY - 2023
Y1 - 2023
N2 - Signal processing (SP) excels at analyzing, processing, and inferring information defined over regular (first continuous, later discrete) domains such as time or space. Indeed, the last 75 years have shown how SP has made an impact in areas such as communications, acoustics, sensing, image processing, and control, to name a few. With the digitalization of the modern world and the increasing pervasiveness of data-collection mechanisms, information of interest in current applications oftentimes arises in non-Euclidean, irregular domains. Graph SP (GSP) generalizes SP tasks to signals living on non-Euclidean domains whose structure can be captured by a weighted graph. Graphs are versatile, able to model irregular interactions, easy to interpret, and endowed with a corpus of mathematical results, rendering them natural candidates to serve as the basis for a theory of processing signals in more irregular domains.
AB - Signal processing (SP) excels at analyzing, processing, and inferring information defined over regular (first continuous, later discrete) domains such as time or space. Indeed, the last 75 years have shown how SP has made an impact in areas such as communications, acoustics, sensing, image processing, and control, to name a few. With the digitalization of the modern world and the increasing pervasiveness of data-collection mechanisms, information of interest in current applications oftentimes arises in non-Euclidean, irregular domains. Graph SP (GSP) generalizes SP tasks to signals living on non-Euclidean domains whose structure can be captured by a weighted graph. Graphs are versatile, able to model irregular interactions, easy to interpret, and endowed with a corpus of mathematical results, rendering them natural candidates to serve as the basis for a theory of processing signals in more irregular domains.
UR - http://www.scopus.com/inward/record.url?scp=85162757109&partnerID=8YFLogxK
U2 - 10.1109/MSP.2023.3262906
DO - 10.1109/MSP.2023.3262906
M3 - Article
AN - SCOPUS:85162757109
SN - 1053-5888
VL - 40
SP - 49
EP - 60
JO - IEEE Signal Processing Magazine
JF - IEEE Signal Processing Magazine
IS - 4
ER -