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

    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 languageEnglish (US)
    Title of host publicationAdvances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference
    Pages257-264
    Number of pages8
    StatePublished - 2009
    Event22nd Annual Conference on Neural Information Processing Systems, NIPS 2008 - Vancouver, BC, Canada
    Duration: Dec 8 2008Dec 11 2008

    Publication series

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

    Other

    Other22nd Annual Conference on Neural Information Processing Systems, NIPS 2008
    CountryCanada
    CityVancouver, BC
    Period12/8/0812/11/08

    ASJC Scopus subject areas

    • Information Systems

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  • Cite this

    Cevher, V., Duarte, M. F., Hegde, C., & Baraniuk, R. G. (2009). Sparse signal recovery using Markov Random Fields. In Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference (pp. 257-264). (Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference).