A residual spectral matching estimator for noisy matrix completion matches the singular values of residuals to those of sparse random matrices and claims minimax optimal error rates.
Bulk universality for generalized Wigner matrices
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abstract
Consider $N\times N$ Hermitian or symmetric random matrices $H$ where the distribution of the $(i,j)$ matrix element is given by a probability measure $\nu_{ij}$ with a subexponential decay. Let $\sigma_{ij}^2$ be the variance for the probability measure $\nu_{ij}$ with the normalization property that $\sum_{i} \sigma^2_{ij} = 1$ for all $j$. Under essentially the only condition that $c\le N \sigma_{ij}^2 \le c^{-1}$ for some constant $c>0$, we prove that, in the limit $N \to \infty$, the eigenvalue spacing statistics of $H$ in the bulk of the spectrum coincide with those of the Gaussian unitary or orthogonal ensemble (GUE or GOE). We also show that for band matrices with bandwidth $M$ the local semicircle law holds to the energy scale $M^{-1}$.
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2024 1verdicts
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Matrix Completion via Residual Spectral Matching
A residual spectral matching estimator for noisy matrix completion matches the singular values of residuals to those of sparse random matrices and claims minimax optimal error rates.