Paper Info
Title
Multiway Graph Signal Processing on Tensors: Integrative Analysis of Irregular Geometries
Abstract
Graph signal processing (GSP) is an important methodology for studying data residing on irregular structures. Because acquired data are increasingly taking the form of multiway tensors, new signal processing tools are needed to maximally utilize the multiway structure within the data. In this article, we review modern signal processing frameworks that generalize GSP to multiway data, starting from graph signals coupled to familiar regular axes, such as time in sensor networks, and then extending to general graphs across all tensor modes. This widely applicable paradigm motivates reformulating and improving classical problems and approaches to creatively address the challenges in tensor-based data. We synthesize common themes arising from current efforts to combine GSP with tensor analysis and highlight future directions in extending GSP to the multiway paradigm.
Year
DOI
Venue
2020
10.1109/MSP.2020.3013555
IEEE Signal Processing Magazine
Keywords
DocType
Volume
multiway graph signal processing,integrative analysis,irregular geometries,GSP,irregular structures,multiway tensors,signal processing tools,multiway structure,modern signal processing frameworks,graph signals,general graphs,tensor modes,tensor-based data,tensor analysis,multiway paradigm,regular axes
Journal
37
Issue
ISSN
Citations 
6
1053-5888
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Jay S. Stanley III102.37
Eric C. Chi2936.89
gal mishne343.45