HDIHT: A High-Accuracy Distributed Iterative Hard Thresholding Algorithm for Compressed Sensing

Abstract

Iterative hard thresholding (IHT) is a beneficial tool for the recovery of sparse vectors in compressed sensing. In this study, we propose a high-accuracy distributed iterative hard thresholding algorithm (HDIHT) with explicit consideration given to the case in which noise is generated. The results of our theoretical analysis show that it is possible to cancel the noise in the HDIHT compared to the IHT. The performance of the HDIHT in the case including noise was equivalent to the classic IHT in the noise-free case. A numerical experiment is also included, and the results are in accordance with the theoretical analysis.

Keywords

• Distributed learning
• iterative hard thresholding
• compressed sensing