Low-rank tensor completion via smooth matrix factorization
From MaRDI portal
Publication:2310630
DOI10.1016/j.apm.2019.02.001zbMath1462.90096OpenAlexW2913935587MaRDI QIDQ2310630
Ting-Zhu Huang, Tai-Xiang Jiang, Yu-Bang Zheng, Teng-Yu Ji, Xi-Le Zhao, Tian-Hui Ma
Publication date: 6 April 2020
Published in: Applied Mathematical Modelling (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.apm.2019.02.001
total variationlow-rank tensor completionframeletblock successive upper-bound minimizationsmooth matrix factorization
Convex programming (90C25) Multilinear algebra, tensor calculus (15A69) Matrix completion problems (15A83)
Related Items
Adaptive total variation and second-order total variation-based model for low-rank tensor completion ⋮ A sheared low-rank model for oblique stripe removal ⋮ Low-rank tensor train for tensor robust principal component analysis ⋮ Cauchy noise removal using group-based low-rank prior ⋮ Remote sensing images destriping using unidirectional hybrid total variation and nonconvex low-rank regularization
Cites Work
- Unnamed Item
- Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
- Tensor Decompositions and Applications
- Parallel matrix factorization for low-rank tensor completion
- Tucker factorization with missing data with application to low-\(n\)-rank tensor completion
- Affine systems in \(L_ 2(\mathbb{R}^d)\): The analysis of the analysis operator
- Cauchy noise removal by nonconvex ADMM with convergence guarantees
- Tensor completion using total variation and low-rank matrix factorization
- Solving a low-rank factorization model for matrix completion by a nonlinear successive over-relaxation algorithm
- Total variation with overlapping group sparsity for deblurring images under Cauchy noise
- Matrix factorization for low-rank tensor completion using framelet prior
- Total variation and high-order total variation adaptive model for restoring blurred images with Cauchy noise
- A non-convex tensor rank approximation for tensor completion
- Hyperspectral image restoration using framelet-regularized low-rank nonnegative matrix factorization
- Deconvolution: a wavelet frame approach
- A framelet-based image inpainting algorithm
- Exact matrix completion via convex optimization
- A Unified Convergence Analysis of Block Successive Minimization Methods for Nonsmooth Optimization
- A New Convex Optimization Model for Multiplicative Noise and Blur Removal
- Tensor completion and low-n-rank tensor recovery via convex optimization
- Truncated $l_{1-2}$ Models for Sparse Recovery and Rank Minimization
- Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
- Framelet-Based Sparse Unmixing of Hyperspectral Images
- Tensor Factorization for Low-Rank Tensor Completion
- Robust Multilinear Tensor Rank Estimation Using Higher Order Singular Value Decomposition and Information Criteria
- Exact Tensor Completion Using t-SVD
- Smooth PARAFAC Decomposition for Tensor Completion
- Tensor Decomposition for Signal Processing and Machine Learning
- Coupled segmentation and denoising/deblurring models for hyperspectral material identification
- Most Tensor Problems Are NP-Hard
