Document Type
Conference Proceeding
Department
Mathematics (CMC)
Publication Date
4-24-2009
Abstract
Compressed sensing has shown that it is possible to reconstruct sparse high dimensional signals from few linear measurements. In many cases, the solution can be obtained by solving an L1-minimization problem, and this method is accurate even in the presence of noise. Recent a modified version of this method, reweighted L1-minimization, has been suggested. Although no provable results have yet been attained, empirical studies have suggested the reweighted version outperforms the standard method. Here we analyze the reweighted L1-minimization method in the noisy case, and provide provable results showing an improvement in the error bound over the standard bounds.
Rights Information
© 2009 Signals, Systems and Computers
Terms of Use & License Information
DOI
10.1109/ACSSC.2009.5470154
Recommended Citation
Needell, D., "Noisy signal recovery via iterative reweighted L1-minimization", Proc. Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA Nov. 2009. doi: 10.1109/ACSSC.2009.5470154
