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) |
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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 |
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Country/Territory | Canada |
City | Vancouver, BC |
Period | 12/8/08 → 12/11/08 |
ASJC Scopus subject areas
- Information Systems