Title :
Thresholded Smoothed
Norm for Accelerated Sparse Recovery
Author :
Han Wang ; Qing Guo ; Gengxin Zhang ; Guangxia Li ; Wei Xiang
Author_Institution :
Sch. of Electron. & Inf. Eng., Harbin Inst. of Technol., Harbin, China
Abstract :
Smoothed ℓ0 norm (SL0) is a fast and complex domain extendible sparse recovery algorithm which is suitable for many practical real-time applications. In this letter, we propose an improved algorithm termed “Thresholded Smoothed ℓ0 Norm (T-SL0)” for accelerating the iterative process of SL0. T-SL0 introduces an iterative efficiency indicator and compares it with a preset threshold in real time to determine whether or not the current iteration should be executed. Through identifying and bypassing low efficient iterations, our approach converges much faster than the original SL0 algorithm. Experimental results are presented to demonstrate that our approach can accelerate SL0 significantly without loss of accuracy.
Keywords :
iterative methods; signal processing; T-SL0 algorithm; extendible sparse recovery algorithm; iterative efficiency indicator; iterative process; thresholded smoothed ℓ0 norm; Acceleration; Accuracy; Indexes; Real-time systems; Shape; Signal processing algorithms; Vectors; Compressive sensing; Compressive sensing (CS); smoothed $boldsymbol{ell_0}$ norm (SL0); smoothed ℓ0 norm (SL0); sparse recovery;
Journal_Title :
Communications Letters, IEEE
DOI :
10.1109/LCOMM.2015.2416711