Recipes on Hard Thresholding Methods

Compressive sensing (CS) is a data acquisition and recovery technique for finding sparse solutions to linear inverse problems from sub-Nyquist measurements. CS features a wide range of computationally efficient and robust signal recovery methods, based on sparsity seeking optimization. In this paper, we present and analyze a class of sparse recovery algorithms, known as hard thresholding methods. We provide optimal strategies on how to set up these algorithms via basic ``ingredients'' for different configurations to achieve complexity vs. accuracy tradeoffs. Simulation results demonstrate notable performance improvements compared to state-of-the-art algorithms both in terms of data reconstruction and computational complexity.

Presented at:
4th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Puerto Rico, December, 2011

 Record created 2013-01-14, last modified 2019-03-16

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