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Paper Detail

Paper IDE.4.3
Paper Title Achievability of nearly-exact alignment for correlated Gaussian databases
Authors Osman Emre Dai, Georgia Institute of Technology, United States; Daniel Cullina, Pennsylvania State University, United States; Negar Kiyavash, Ecole Polytechnique Fédérale de Lausanne, Switzerland
Session E.4: Estimation and Applications
Presentation Lecture
Track Detection and Estimation
Manuscript  Click here to download the manuscript
Virtual Presentation  Click here to watch in the Virtual Symposium
Abstract 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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2021 IEEE International Symposium on Information Theory

11-16 July 2021 | Melbourne, Victoria, Australia

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