Stochastic generalized sampling uses leverage-score sampling and a new matrix Bernstein inequality to guarantee stable recovery at m ≳ n log n samples with high probability, even for redundant frames, and demonstrates near-exponential convergence on analytic function recovery from Fourier data.
On some extensions of bernstein’s inequality for self-adjoint operators.Statistics & Prob- ability Letters, 127:111–119, 2017
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Stochastic Generalized Sampling
Stochastic generalized sampling uses leverage-score sampling and a new matrix Bernstein inequality to guarantee stable recovery at m ≳ n log n samples with high probability, even for redundant frames, and demonstrates near-exponential convergence on analytic function recovery from Fourier data.