Robust 1-bit Compressive Sensing via Gradient Support Pursuit
arXiv:1304.6627
Abstract
This paper studies a formulation of 1-bit Compressed Sensing (CS) problem based on the maximum likelihood estimation framework. In order to solve the problem we apply the recently proposed Gradient Support Pursuit algorithm, with a minor modification. Assuming the proposed objective function has a Stable Restricted Hessian, the algorithm is shown to accurately solve the 1-bit CS problem. Furthermore, the algorithm is compared to the state-of-the-art 1-bit CS algorithms through numerical simulations. The results suggest that the proposed method is robust to noise and at mid to low input SNR regime it achieves the best reconstruction SNR vs. execution time trade-off.
18 pages, 5 figures
Cited by in corpus (5)
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- Nonconvex penalties with analytical solutions for one-bit compressive sensing
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- One-Bit Compressed Sensing via One-Shot Hard Thresholding