Low-rank approximation algorithms for matrix completion with random sampling

DOI10.1134/S0965542521050122zbMath1469.65082MaRDI QIDQ2038493

O. S. Lebedeva, A. I. Osinsky, Sergey V. Petrov

Publication date: 7 July 2021

Published in: Computational Mathematics and Mathematical Physics (Search for Journal in Brave)

zbMATH Keywords

matrix completionrandom subspaceslow-rank matricescross approximation methodsingular value projection

Mathematics Subject Classification ID

Matrix completion problems (15A83) Numerical methods for low-rank matrix approximation; matrix compression (65F55)

Related Items

Matrix completion with sparse measurement errors, New applications of matrix methods

Cites Work

• Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
• Rectangular submatrices of maximum volume and their computation
• New accuracy estimates for pseudoskeleton approximations of matrices
• Convex multi-task feature learning
• A theory of pseudoskeleton approximations
• Pseudo-skeleton approximations with better accuracy estimates
• Normalized Iterative Hard Thresholding for Matrix Completion
• Compressive Multiplexing of Correlated Signals
• Low-Rank Matrix Approximation Using the Lanczos Bidiagonalization Process with Applications
• Rank Awareness in Joint Sparse Recovery
• Near-Optimal Column-Based Matrix Reconstruction