Non-convex low-rank matrix recovery with arbitrary outliers via median-truncated gradient descent

DOI10.1093/imaiai/iaz009zbMath1471.62376arXiv1709.08114OpenAlexW2963685382WikidataQ128120399 ScholiaQ128120399MaRDI QIDQ5006521

Huishuai Zhang, Yingbin Liang, Yuan-Xin Li, Yuejie Chi

Publication date: 16 August 2021

Published in: Information and Inference: A Journal of the IMA (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1709.08114

zbMATH Keywords

outliersrobust algorithmslow-rank matrix recoverymedian-truncated gradient descentnon-convex approach

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Statistics of extreme values; tail inference (62G32) Large deviations (60F10)

Related Items (5)

Quantile-Based Iterative Methods for Corrupted Systems of Linear Equations ⋮ Analytical convergence regions of accelerated gradient descent in nonconvex optimization under regularity condition ⋮ GNMR: A Provable One-Line Algorithm for Low Rank Matrix Recovery ⋮ Nonconvex Robust Low-Rank Matrix Recovery ⋮ Low rank matrix recovery with adversarial sparse noise*

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