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.
On the subspaces ofl p (p >2) spanned by sequences of independent random variables.Israel Journal of Mathematics, 8(3):273–303, 1970
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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.