Sparse signal recovery using Markov Random Fields

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

113 Scopus citations


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 languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference
Number of pages8
StatePublished - Dec 1 2009
Event22nd Annual Conference on Neural Information Processing Systems, NIPS 2008 - Vancouver, BC, Canada
Duration: Dec 8 2008Dec 11 2008


Other22nd Annual Conference on Neural Information Processing Systems, NIPS 2008
CityVancouver, BC

ASJC Scopus subject areas

  • Information Systems


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