Nuclear norm minimization and semidefinite-constrained ERM achieve optimal O(rn) sample complexity for low-rank matrix recovery under heavy-tailed quadratic sampling with only finite 4+δ moments.
Rieman- nian thresholding methods for row-sparse and low-rank matrix recovery.Numerical Algorithms, 93(2):669–693, 2023
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Low-Rank Matrix Recovery via Heavy-Tailed Quadratic Sampling
Nuclear norm minimization and semidefinite-constrained ERM achieve optimal O(rn) sample complexity for low-rank matrix recovery under heavy-tailed quadratic sampling with only finite 4+δ moments.