Imbalanced low-rank tensor completion via latent matrix factorization
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Publication:6488731
DOI10.1016/J.NEUNET.2022.08.023WikidataQ114145530 ScholiaQ114145530MaRDI QIDQ6488731
Yuning Qiu, Sheng-Li Xie, Guoxu Zhou, Junhua Zeng, Qibin Zhao
Publication date: 18 October 2023
Published in: Neural Networks (Search for Journal in Brave)
tensor analysistensor completionlow-rank tensor recoverytensor ring decompositionimage/video inpainting
Cites Work
- Tensor Decompositions and Applications
- Parallel matrix factorization for low-rank tensor completion
- Convergence of descent methods for semi-algebraic and tame problems: proximal algorithms, forward-backward splitting, and regularized Gauss-Seidel methods
- Multiple graphs learning with a new weighted tensor nuclear norm
- Proximal Alternating Minimization and Projection Methods for Nonconvex Problems: An Approach Based on the Kurdyka-Łojasiewicz Inequality
- Tensor rank is NP-complete
- Clarke Subgradients of Stratifiable Functions
- Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
- Efficient Tensor Completion for Color Image and Video Recovery: Low-Rank Tensor Train
- Smooth PARAFAC Decomposition for Tensor Completion
- The Łojasiewicz Inequality for Nonsmooth Subanalytic Functions with Applications to Subgradient Dynamical Systems
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