Sparse signal recovery using Markov Random Fields

Volkan Cevher; Marco F. Duarte; Chinmay Hegde; Richard G. Baraniuk

Sparse signal recovery using Markov Random Fields

Volkan Cevher, Marco F. Duarte, Chinmay Hegde, Richard G. Baraniuk

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Compressive Sensing (CS) combines sampling and compression into a single sub- Nyquist linear measurement process for sparse and compressible signals. In this paper, we extend the theory of CS to include signals that are concisely represented in terms of a graphicalmodel. In particular, we use Markov Random Fields (MRFs) to represent sparse signals whose nonzero coefficients are clustered. Our new model-based recovery algorithm, dubbed Lattice Matching Pursuit (LaMP), stably recovers MRF-modeled signals using many fewer measurements and computations than the current state-of-the-art algorithms.

Original language	English (US)
Title of host publication	Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference
Pages	257-264
Number of pages	8
State	Published - Dec 1 2009
Event	22nd Annual Conference on Neural Information Processing Systems, NIPS 2008 - Vancouver, BC, Canada Duration: Dec 8 2008 → Dec 11 2008

Other

Other	22nd Annual Conference on Neural Information Processing Systems, NIPS 2008
Country	Canada
City	Vancouver, BC
Period	12/8/08 → 12/11/08

ASJC Scopus subject areas

Information Systems

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title = "Sparse signal recovery using Markov Random Fields",

abstract = "Compressive Sensing (CS) combines sampling and compression into a single sub- Nyquist linear measurement process for sparse and compressible signals. In this paper, we extend the theory of CS to include signals that are concisely represented in terms of a graphicalmodel. In particular, we use Markov Random Fields (MRFs) to represent sparse signals whose nonzero coefficients are clustered. Our new model-based recovery algorithm, dubbed Lattice Matching Pursuit (LaMP), stably recovers MRF-modeled signals using many fewer measurements and computations than the current state-of-the-art algorithms.",

author = "Volkan Cevher and Duarte, {Marco F.} and Chinmay Hegde and Baraniuk, {Richard G.}",

year = "2009",

month = dec,

day = "1",

language = "English (US)",

isbn = "9781605609492",

pages = "257--264",

booktitle = "Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference",

note = "22nd Annual Conference on Neural Information Processing Systems, NIPS 2008 ; Conference date: 08-12-2008 Through 11-12-2008",

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T1 - Sparse signal recovery using Markov Random Fields

AU - Cevher, Volkan

AU - Duarte, Marco F.

AU - Hegde, Chinmay

AU - Baraniuk, Richard G.

PY - 2009/12/1

Y1 - 2009/12/1

N2 - Compressive Sensing (CS) combines sampling and compression into a single sub- Nyquist linear measurement process for sparse and compressible signals. In this paper, we extend the theory of CS to include signals that are concisely represented in terms of a graphicalmodel. In particular, we use Markov Random Fields (MRFs) to represent sparse signals whose nonzero coefficients are clustered. Our new model-based recovery algorithm, dubbed Lattice Matching Pursuit (LaMP), stably recovers MRF-modeled signals using many fewer measurements and computations than the current state-of-the-art algorithms.

AB - Compressive Sensing (CS) combines sampling and compression into a single sub- Nyquist linear measurement process for sparse and compressible signals. In this paper, we extend the theory of CS to include signals that are concisely represented in terms of a graphicalmodel. In particular, we use Markov Random Fields (MRFs) to represent sparse signals whose nonzero coefficients are clustered. Our new model-based recovery algorithm, dubbed Lattice Matching Pursuit (LaMP), stably recovers MRF-modeled signals using many fewer measurements and computations than the current state-of-the-art algorithms.

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M3 - Conference contribution

AN - SCOPUS:84858788674

SN - 9781605609492

SP - 257

EP - 264

BT - Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference

T2 - 22nd Annual Conference on Neural Information Processing Systems, NIPS 2008

Y2 - 8 December 2008 through 11 December 2008

ER -