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Fast Iterative Hard Thresholding for Compressed Sensing
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DOI：10.1109/LSP.2014.2364851.png)
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摘要
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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.
Keyword:
1-ALPS(2)
compressed sensing
FIHT
restricted isometry constants
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9.6
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1.1W
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1.7W
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