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Robust 1-bit Compressed Sensing with Iterative Hard Thresholding
In particular, we analyze the Binary Iterative Hard Thresholding (BIHT) algorithm, a proximal gradient descent on a properly defined loss function used for 1-bit compressed sensing, in this noisy setting.
Improved Support Recovery in Universal One-bit Compressed Sensing
A {\em universal} measurement matrix for 1bCS refers to one set of measurements that work for all sparse signals.
Binary Iterative Hard Thresholding Converges with Optimal Number of Measurements for 1-Bit Compressed Sensing
Note that, this dependence on $k$ and $\epsilon$ is optimal for any recovery method in 1-bit compressed sensing.
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