On the Complexity of Robust PCA and ℓ1-Norm Low-Rank Matrix Approximation

DOI10.1287/moor.2017.0895zbMath1434.65054arXiv1509.09236OpenAlexW3122766110MaRDI QIDQ5219689

Nicolas Gillis, Stephen A. Vavasis

Publication date: 12 March 2020

Published in: Mathematics of Operations Research (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1509.09236

zbMATH Keywords

computational complexitycut normlow-rank matrix approximationsbinary matrix factorization

Mathematics Subject Classification ID

Factorization of matrices (15A23) Numerical methods for low-rank matrix approximation; matrix compression (65F55)

Related Items

Off-diagonal symmetric nonnegative matrix factorization, Background subtraction with Kronecker-basis-representation based tensor sparsity and \(l_{1,1,2}\) norm, Nonsmooth rank-one matrix factorization landscape, Certifying the Absence of Spurious Local Minima at Infinity, Sufficient Conditions for Instability of the Subgradient Method with Constant Step Size, Unnamed Item, Low-Rank Binary Matrix Approximation in Column-Sum Norm., Low-rank matrix approximation in the infinity norm, Binary Component Decomposition Part I: The Positive-Semidefinite Case

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