Accelerated Hard Thresholding Algorithms for Sparse Recovery
Xueci Zhao, Peibing Du, Tao Sun, Lizhi Cheng
Available Online April 2017.
- https://doi.org/10.2991/fmsmt-17.2017.259How to use a DOI?
- Hard thresholding pursuit, restricted isometry property, compressive sensing, optimization methods.
- In linear observations, i.e., a system of linear equations ( ), the hard thresholding pursuit (HTP) is used to find a sparse signal. HTP is an iterative algorithm that has been found many applications in compressive sensing, due to its good recovery performance, which includes linear convergence speed, high recovery rate, and stability. In this paper, we further develop accelerated algorithms to deal with a linear least square (LLS) problem in each iteration. Theoretically, we prove that all these algorithms are convergent, provided that the sensing matrix has suitable restricted isometry property. Numerical experiments on sparse signal recovery demonstrate the efficiency of the proposed methods.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Xueci Zhao AU - Peibing Du AU - Tao Sun AU - Lizhi Cheng PY - 2017/04 DA - 2017/04 TI - Accelerated Hard Thresholding Algorithms for Sparse Recovery BT - Proceedings of the 2017 5th International Conference on Frontiers of Manufacturing Science and Measuring Technology (FMSMT 2017) PB - Atlantis Press SP - 1322 EP - 1328 SN - 2352-5401 UR - https://doi.org/10.2991/fmsmt-17.2017.259 DO - https://doi.org/10.2991/fmsmt-17.2017.259 ID - Zhao2017/04 ER -