Conference proceeding
Fast alternating projected gradient descent algorithms for recovering spectrally sparse signals
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vol.2016-, pp.4638-4642
03/2016
DOI: 10.1109/ICASSP.2016.7472556
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.
Details
- Title: Subtitle
- Fast alternating projected gradient descent algorithms for recovering spectrally sparse signals
- Creators
- Myung Cho - University of IowaJian-Feng Cai - Dept. of Math., Hong Kong Univ. of Sci. & Technol., Hong Kong, ChinaSuhui Liu - University of IowaYonina C Eldar - Technion – Israel Institute of TechnologyWeiyu Xu - University of Iowa
- Resource Type
- Conference proceeding
- Publication Details
- 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vol.2016-, pp.4638-4642
- Publisher
- IEEE
- DOI
- 10.1109/ICASSP.2016.7472556
- ISSN
- 1520-6149
- eISSN
- 2379-190X
- Language
- English
- Date published
- 03/2016
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984197114702771
Metrics
15 Record Views