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CORR
2010
Springer

Graphical Models Concepts in Compressed Sensing

13 years 2 months ago
Graphical Models Concepts in Compressed Sensing
This paper surveys recent work in applying ideas from graphical models and message passing algorithms to solve large scale regularized regression problems. In particular, the focus is on compressed sensing reconstruction via ℓ1 penalized least-squares (known as LASSO or BPDN). We discuss how to derive fast approximate message passing algorithms to solve this problem. Surprisingly, the analysis of such algorithms allows to prove exact high-dimensional limit results for the LASSO risk. This paper will appear as a chapter in a book on ‘Compressed Sensing’ edited by Yonina Eldar and Gitta Kutynok.
Andrea Montanari
Added 24 Jan 2011
Updated 24 Jan 2011
Type Journal
Year 2010
Where CORR
Authors Andrea Montanari
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