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    Fast alternating projected gradient descent algorithms for recovering spectrally sparse signals

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    Type
    Conference Paper
    Authors
    Cho, Myung
    Cai, Jian-Feng
    Liu, Suhui
    Eldar, Yonina C.
    Xu, Weiyu
    Date
    2016-06-24
    Online Publication Date
    2016-06-24
    Print Publication Date
    2016-03
    Permanent link to this record
    http://hdl.handle.net/10754/623543
    
    Metadata
    Show full item record
    Abstract
    We 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.
    Citation
    Cho 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.
    Sponsors
    The work of W. Xu was supported by Simons Foundation, Iowa Energy Center, KAUST, NIH 1R01EB020665-01.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
    Conference/Event name
    41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
    DOI
    10.1109/icassp.2016.7472556
    ae974a485f413a2113503eed53cd6c53
    10.1109/icassp.2016.7472556
    Scopus Count
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