Fast alternating projected gradient descent algorithms for recovering spectrally sparse signals
Online Publication Date2016-06-24
Print Publication Date2016-03
Permanent link to this recordhttp://hdl.handle.net/10754/623543
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AbstractWe propose fast algorithms that speed up or improve the performance of recovering spectrally sparse signals from un-derdetermined measurements. Our algorithms are based on a non-convex approach of using alternating projected gradient descent for structured matrix recovery. We apply this approach to two formulations of structured matrix recovery: Hankel and Toeplitz mosaic structured matrix, and Hankel structured matrix. Our methods provide better recovery performance, and faster signal recovery than existing algorithms, including atomic norm minimization.
CitationCho M, Cai J-F, Liu S, Eldar YC, Xu W (2016) Fast alternating projected gradient descent algorithms for recovering spectrally sparse signals. 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Available: http://dx.doi.org/10.1109/icassp.2016.7472556.
SponsorsThe work of W. Xu was supported by Simons Foundation, Iowa Energy Center, KAUST, NIH 1R01EB020665-01.
Conference/Event name41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016