Title :
Fast Iterative Hard Thresholding for Compressed Sensing
Author_Institution :
Dept. of Math., Hong Kong Univ. of Sci. & Technol., Kowloon, China
Abstract :
Algebraic Pursuit (ALPS) is an effective class of iterative hard thresholding algorithms for compressed sensing, with 1-ALPS(2) being the most computationally efficient variant of ALPS. We present a proof of convergence, using restricted isometry constants, for 1-ALPS(2) as well as the recently introduced Fast Iterative Hard Thresholding (FIHT). Large scale empirical testing shows FIHT is superior to 1-ALPS(2) in terms of both the sizes of the problems that are recoverable and overall computational time.
Keywords :
compressed sensing; iterative methods; ALPS; FIHT; algebraic pursuit; compressed sensing; fast iterative hard thresholding; large scale empirical testing; restricted isometry constants; Approximation algorithms; Compressed sensing; Discrete cosine transforms; Sensors; Signal processing algorithms; Sparse matrices; Vectors; 1-ALPS(2); FIHT; compressed sensing; restricted isometry constants;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2014.2364851
