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Hauptverfasser: Yang, Yuepeng, Ma, Cong
Format: Preprint
Veröffentlicht: 2022
Schlagworte:
Online-Zugang:https://arxiv.org/abs/2207.05802
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author Yang, Yuepeng
Ma, Cong
author_facet Yang, Yuepeng
Ma, Cong
contents This paper is concerned with noisy matrix completion--the problem of recovering a low-rank matrix from partial and noisy entries. Under uniform sampling and incoherence assumptions, we prove that a tuning-free square-root matrix completion estimator (square-root MC) achieves optimal statistical performance for solving the noisy matrix completion problem. Similar to the square-root Lasso estimator in high-dimensional linear regression, square-root MC does not rely on the knowledge of the size of the noise. While solving square-root MC is a convex program, our statistical analysis of square-root MC hinges on its intimate connections to a nonconvex rank-constrained estimator.
format Preprint
id arxiv_https___arxiv_org_abs_2207_05802
institution arXiv
publishDate 2022
record_format arxiv
spellingShingle Optimal tuning-free convex relaxation for noisy matrix completion
Yang, Yuepeng
Ma, Cong
Statistics Theory
Information Theory
This paper is concerned with noisy matrix completion--the problem of recovering a low-rank matrix from partial and noisy entries. Under uniform sampling and incoherence assumptions, we prove that a tuning-free square-root matrix completion estimator (square-root MC) achieves optimal statistical performance for solving the noisy matrix completion problem. Similar to the square-root Lasso estimator in high-dimensional linear regression, square-root MC does not rely on the knowledge of the size of the noise. While solving square-root MC is a convex program, our statistical analysis of square-root MC hinges on its intimate connections to a nonconvex rank-constrained estimator.
title Optimal tuning-free convex relaxation for noisy matrix completion
topic Statistics Theory
Information Theory
url https://arxiv.org/abs/2207.05802