Fast way to rebuild signals with fewer measurements
This short note talks about a simple idea that can pull a clear picture from very few data points, like seeing a full song from tiny bits.
Called compressed sensing, the trick is to measure less and still get nearly the same result.
A straightforward method known as iterative hard thresholding works like a smart filter that picks the most important pieces and throws away the rest.
It often gives near-optimal results, stays robust when the measurements are noisy, and needs only a handful of samples to work.
The method runs fast and each step costs about as much as taking the measurements, so it's useful where speed matters.
It uses very little storage so it fits on small devices, remember that low memory is a big win for many projects, and that is why it's low memory friendly.
You can use it with many ways of sampling data, the performance mostly depends on how sparse the true signal is and the measurement setup.
Try to think of it as a simple, fast way to recover the whole from very little, its often surprising.
fast
Read article comprehensive review in Paperium.net:
Iterative Hard Thresholding for Compressed Sensing
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