Noisy low-rank matrix completion with general sampling distribution
type: Dataset
description: In the present paper we consider the problem of matrix completion with noise for general sampling schemes. Unlike previous works, in our construction we do not need to know or to evaluate the sampling distribution or the variance of the noise. We propose new nuclear-norm penalized estimators, one of them of the ''square-root'' type. We prove that, up to a logarithmic factor, our estimators achieve optimal rates with respect to the estimation error.
landing page: 1350-7265
keyword: 62j99-62h12-60b20-15a83
keyword: high-dimensional-sparse-model
keyword: infoeu-reposemanticsarticle
keyword: journal-articles
keyword: low-rank-matrix-estimation
keyword: mathmath-stmathematics-mathstatistics-mathst
keyword: matrix-completion
keyword: statthstatistics-statstatistics-theory-statth
keyword: unknown-variance
issued: 2026-05-15T17:39:48.687273
modified: 2026-05-15T17:39:48.687282
language: en
contact point: Organization
  fn: CCSD

test_moissonnage_selune
type: Agent

Noisy low-rank matrix completion with general sampling distribution
type: Distribution
format: HTML
access URL: https://hal.science/hal-00675413
issued: 2026-05-15T17:39:48.758715
modified: 2026-05-15T17:39:48.656667