# Relaxed leverage sampling for low-rank matrix completion

@article{Kundu2017RelaxedLS,
title={Relaxed leverage sampling for low-rank matrix completion},
author={Abhisek Kundu},
journal={Inf. Process. Lett.},
year={2017},
volume={124},
pages={6-9}
}
• Abhisek Kundu
• Published 2017
• Mathematics, Computer Science
• Inf. Process. Lett.
We consider the problem of exact recovery of any $m\times n$ matrix of rank $\varrho$ from a small number of observed entries via the standard nuclear norm minimization framework. Such low-rank matrices have degrees of freedom $(m+n)\varrho - \varrho^2$. We show that any arbitrary low-rank matrices can be recovered exactly from a $\Theta\left(((m+n)\varrho - \varrho^2)\log^2(m+n)\right)$ randomly sampled entries, thus matching the lower bound on the required number of entries (in terms of… Expand
2 Citations

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