Robust tensor recovery with nonconvex and nonsmooth regularization

DOI10.1016/j.amc.2022.127566OpenAlexW4298113505MaRDI QIDQ2096313

Yanyan Li

Publication date: 16 November 2022

Published in: Applied Mathematics and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.amc.2022.127566

zbMATH Keywords

nonconvexnonsmoothrobust tensor recoverytensor average rank

Mathematics Subject Classification ID

Mathematical programming (90Cxx) Numerical linear algebra (65Fxx) Basic linear algebra (15Axx)

Uses Software

• BSDS
• tproduct

Cites Work

• Tensor Decompositions and Applications
• Tensor-Train Decomposition
• A rank-corrected procedure for matrix completion with fixed basis coefficients
• Two-stage convex relaxation approach to low-rank and sparsity regularized least squares loss
• Equivalent Lipschitz surrogates for zero-norm and rank optimization problems
• Low-rank tensor completion using matrix factorization based on tensor train rank and total variation
• Low rank Tucker-type tensor approximation to classical potentials
• Analysis of individual differences in multidimensional scaling via an \(n\)-way generalization of ``Eckart-Young decomposition
• Robust principal component analysis?
• Tensor rank is NP-complete
• Exact Tensor Completion Using t-SVD
• Low-Tubal-Rank Tensor Completion Using Alternating Minimization
• A Convergent 3-Block SemiProximal Alternating Direction Method of Multipliers for Conic Programming with 4-Type Constraints
• Third-Order Tensors as Operators on Matrices: A Theoretical and Computational Framework with Applications in Imaging
• Nonlocal robust tensor recovery with nonconvex regularization ^*

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