Mixed norm regularized models for low-rank tensor completion
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Publication:6544608
DOI10.1016/j.ins.2024.120630zbMATH Open1540.65136MaRDI QIDQ6544608
Yuanyang Bu, Jonathan Cheung-Wai Chan, Yongqiang Zhao
Publication date: 27 May 2024
Published in: Information Sciences (Search for Journal in Brave)
Multilinear algebra, tensor calculus (15A69) Numerical linear algebra (65F99) Matrix completion problems (15A83)
Cites Work
- Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
- Tensor Decompositions and Applications
- Tucker factorization with missing data with application to low-\(n\)-rank tensor completion
- Tensor completion using total variation and low-rank matrix factorization
- Tensor factorization with total variation and Tikhonov regularization for low-rank tensor completion in imaging data
- Iterative \(p\)-shrinkage thresholding algorithm for low Tucker rank tensor recovery
- Nonconvex tensor rank minimization and its applications to tensor recovery
- Robust Schatten-\(p\) norm based approach for tensor completion
- A non-convex tensor rank approximation for tensor completion
- MRI denoising via sparse tensors with reweighted regularization
- Low-rank tensor completion via smooth matrix factorization
- Low-rank tensor completion using matrix factorization based on tensor train rank and total variation
- Robust Low-Rank Matrix Completion by Riemannian Optimization
- A Unified Convergence Analysis of Block Successive Minimization Methods for Nonsmooth Optimization
- Exact Tensor Completion Using t-SVD
- Smooth PARAFAC Decomposition for Tensor Completion
- Framelet Representation of Tensor Nuclear Norm for Third-Order Tensor Completion
- Matrix Completion From a Few Entries
- Third-Order Tensors as Operators on Matrices: A Theoretical and Computational Framework with Applications in Imaging
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