Tensor Completion via Gaussian Process--Based Initialization
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Publication:5147973
DOI10.1137/19M1306518MaRDI QIDQ5147973
Ivan V. Oseledets, Evgeny Burnaev, Y. Kapushev
Publication date: 29 January 2021
Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1912.05179
Related Items (2)
Black Box Approximation in the Tensor Train Format Initialized by ANOVA Decomposition ⋮ Proximal gradient algorithm for nonconvex low tubal rank tensor recovery
Cites Work
- Tensor-Train Decomposition
- TT-cross approximation for multidimensional arrays
- Computationally efficient algorithm for Gaussian process regression in case of structured samples
- Low-rank tensor completion by Riemannian optimization
- Incomplete cross approximation in the mosaic-skeleton method
- Riemannian Optimization for High-Dimensional Tensor Completion
- Tensor completion and low-n-rank tensor recovery via convex optimization
- Variants of Alternating Least Squares Tensor Completion in the Tensor Train Format
- Hierarchical Tensor Approximation of Output Quantities of Parameter-Dependent PDEs
- Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
- Smooth PARAFAC Decomposition for Tensor Completion
- Fast and Accurate Tensor Completion With Total Variation Regularized Tensor Trains
- Advanced Lectures on Machine Learning
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