Compressed sensing: L0, L2 and no L1.
I heard Joel Tropp talk recently on his result with Deanna Needell. The problem involves measuring the signal with a small number of inner products and using these measurements to pick the linear combination of a small number of basis vectors that approximates the signal the best. Group testing and greedy methods (L0 approach) or linear programming (L1) have been in vogue recently. A different method (recent ex) iteratively refines a set of vectors (L0) and each time finds best linear combination by least squares optimization (L2). The Needell and Tropp result follows this approach, but uses the structural properties of the set of measurements to get nice bounds for the problem; this method may be among the most suitable for current technologies that perform these measurements.
posted by metoo at 5:05 PM
4 Comments:
The link to Joel Tropp's paper seems not working....
Well i guess you have a problem but in somewhere else, when we talk about programming could be anything even the team you have working with you, but usually the problem is with the programmers most of them are like frikis fighting about who knows more...
Hello!! I really like it, Joel Tropp is so cool, his live is really interesting, I have been reading about it since many years ago, thanks for sharing!22dd
INteresting point of view, you are right in the measurements.
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