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.
Higher order fluctuations of extremal eigenvalues of sparse random matrices
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
We consider extremal eigenvalues of sparse random matrices, a class of random matrices including the adjacency matrices of Erd\H{o}s-R\'{e}nyi graphs $\mathcal{G}(N,p)$. Recently, it was shown that the leading order fluctuations of extremal eigenvalues are given by a single random variable associated with the total degree of the graph (Ann. Probab., 48(2):916-962, 2020; Probab. Theory Related Fields, 180:985-1056, 2021). We construct a sequence of random correction terms to capture higher (sub-leading) order fluctuations of extremal eigenvalues in the regime $N^{\epsilon} < pN < N^{1/3-\epsilon}$. Using these random correction terms, we prove a local law up to a shifted edge and recover the rigidity of extremal eigenvalues under some corrections for $pN>N^{\epsilon}$.
fields
stat.ML 1years
2024 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
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.