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Expander codes over reals, Euclidean sections, and compressed sensing
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Adaptive compressed sensing of speech signal based on data-driven dictionary
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Automatica (Journal of IFAC)
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Hi-index | 756.49 |
This paper considers a natural error correcting problem with real valued input/output. We wish to recover an input vector f∈Rn from corrupted measurements y=Af+e. Here, A is an m by n (coding) matrix and e is an arbitrary and unknown vector of errors. Is it possible to recover f exactly from the data y? We prove that under suitable conditions on the coding matrix A, the input f is the unique solution to the ℓ1-minimization problem (||x||ℓ1:=Σi|xi|) min(g∈Rn) ||y - Ag||ℓ1 provided that the support of the vector of errors is not too large, ||e||ℓ0:=|{i:ei ≠ 0}|≤ρ·m for some ρ0. In short, f can be recovered exactly by solving a simple convex optimization problem (which one can recast as a linear program). In addition, numerical experiments suggest that this recovery procedure works unreasonably well; f is recovered exactly even in situations where a significant fraction of the output is corrupted. This work is related to the problem of finding sparse solutions to vastly underdetermined systems of linear equations. There are also significant connections with the problem of recovering signals from highly incomplete measurements. In fact, the results introduced in this paper improve on our earlier work. Finally, underlying the success of ℓ1 is a crucial property we call the uniform uncertainty principle that we shall describe in detail.