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Learning graphs from data: a signal representation perspective

Abstract:

The construction of a meaningful graph topology plays a crucial role in the effective representation, processing, analysis, and visualization of structured data. When a natural choice of the graph is not readily available from the data sets, it is thus desirable to infer or learn a graph topology from the data. In this article, we survey solutions to the problem of graph learning, including classical viewpoints from statistics and physics, and more recent approaches that adopt a graph signal ...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Accepted Manuscript

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Publisher copy:
10.1109/MSP.2018.2887284

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Role:
Author
Publisher:
IEEE Publisher's website
Journal:
IEEE Signal Processing Magazine Journal website
Volume:
36
Issue:
3
Pages:
44-63
Publication date:
2019-04-26
Acceptance date:
2018-11-16
DOI:
EISSN:
1558-0792
ISSN:
1053-5888
Pubs id:
pubs:962997
URN:
uri:81fc7c01-be75-41a1-b489-19815d114a5e
UUID:
uuid:81fc7c01-be75-41a1-b489-19815d114a5e
Local pid:
pubs:962997

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