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research article

Sparse Sampling for Inverse Problems With Tensors

Ortiz-Jimenez, Guillermo  
•
Coutino, Mario
•
Chepuri, Sundeep Prabhakar
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June 15, 2019
IEEE Transactions On Signal Processing

We consider the problem of designing sparse sampling strategies for multidomain signals, which can be represented using tensors that admit a known multilinear decomposition. We leverage the multidomain structure of tensor signals and propose to acquire samples using a Kronecker-structured sensing function, thereby circumventing the curse of dimensionality. For designing such sensing functions, we develop low-complexity greedy algorithms based on submodular optimization methods to compute near-optimal sampling sets. We present several numerical examples, ranging from multiantenna communications to graph signal processing, to validate the developed theory.

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Type
research article
DOI
10.1109/TSP.2019.2914879
Web of Science ID

WOS:000469369900003

Author(s)
Ortiz-Jimenez, Guillermo  
Coutino, Mario
Chepuri, Sundeep Prabhakar
Leus, Geert
Date Issued

2019-06-15

Published in
IEEE Transactions On Signal Processing
Volume

67

Issue

12

Start page

3272

End page

3286

Subjects

Engineering, Electrical & Electronic

•

Engineering

•

graph signal processing

•

multidimensional sampling

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sparse sampling

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submodular optimization

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tensors

•

sensor selection

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gaussian detection

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approximations

•

placement

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LTS4  
Available on Infoscience
June 18, 2019
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/157516
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