Derives explicit reconstruction error bounds for inverse problems on Riemannian manifolds from Marcinkiewicz-Zygmund point samples, with detailed results for convolutions on two-point homogeneous spaces including the sphere.
Compressed sensing for in- verse problems II: applications to deconvolution, source recovery, and MRI
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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.
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Sampling theorems for inverse problems on Riemannian manifolds
Derives explicit reconstruction error bounds for inverse problems on Riemannian manifolds from Marcinkiewicz-Zygmund point samples, with detailed results for convolutions on two-point homogeneous spaces including the sphere.
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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.