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  4. Achievability of nearly-exact alignment for correlated Gaussian databases
 
conference paper

Achievability of nearly-exact alignment for correlated Gaussian databases

Dai, Osman Emre
•
Kiyavash, Negar  
•
Cullina, Daniel
2020
2020 IEEE International Symposium on Information Theory (ISIT)
2020 IEEE International Symposium on Information Theory

We study the conditions that allow for the alignment of correlated databases with multivariate Gaussian features. We present some analysis tools that allow us to go beyond the achievability result for exact alignment and derive the condition for nearly-exact alignment. Our main theorem gives an expression for the order of magnitude of the error in alignment as a function of mutual information between features.

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Type
conference paper
DOI
10.1109/ISIT44484.2020.9174507
Web of Science ID

WOS:000714963401052

Author(s)
Dai, Osman Emre
Kiyavash, Negar  
Cullina, Daniel
Date Issued

2020

Publisher

IEEE

Publisher place

New York

Published in
2020 IEEE International Symposium on Information Theory (ISIT)
ISBN of the book

978-1-7281-6432-8

Series title/Series vol.

IEEE International Symposium on Information Theory

Start page

1230

End page

1235

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
BAN  
Event nameEvent placeEvent date
2020 IEEE International Symposium on Information Theory

Los Angeles, CA, USA, Virtual Conference

June 21-26, 2020

Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/174774
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