Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?

@article{Cands2004NearOptimalSR,
  title={Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?},
  author={Emmanuel J. Cand{\`e}s and Terence Tao},
  journal={IEEE Transactions on Information Theory},
  year={2004},
  volume={52},
  pages={5406-5425}
}
Suppose we are given a vector f in a class FsubeRopf<sup>N </sup>, e.g., a class of digital signals or digital images. How many linear measurements do we need to make about f to be able to recover f to within precision epsi in the Euclidean (lscr<sub>2</sub>) metric? This paper shows that if the objects of interest are sparse in a fixed basis or compressible, then it is possible to reconstruct f to within very high accuracy from a small number of random measurements by solving a simple linear… CONTINUE READING

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