In this paper, we propose an algorithm referred to as multipath matching pursuit (MMP) that investigates multiple promising candidates to recover sparse signals from compressed measurements. Our method is inspired by the fact that the problem to find the candidate that minimizes the residual is readily modeled as a combinatoric tree search problem and the greedy search strategy is a good fit for solving this problem. In the empirical results as well as the restricted isometry property-based performance guarantee, we show that the proposed MMP algorithm is effective in reconstructing original sparse signals for both noiseless and noisy scenarios.
Multipath Matching Pursuit
Seokbeop Kwon,Jian Wang,B. Shim
Published 2013 in IEEE Transactions on Information Theory
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- Publication year
2013
- Venue
IEEE Transactions on Information Theory
- Publication date
2013-08-22
- Fields of study
Mathematics, Computer Science, Engineering
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