Hot-SVD: higher order t-singular value decomposition for tensors based on tensor-tensor product
From MaRDI portal
Publication:2099552
DOI10.1007/s40314-022-02107-7OpenAlexW4309197400MaRDI QIDQ2099552
Publication date: 24 November 2022
Published in: Computational and Applied Mathematics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2204.10229
Nonconvex programming, global optimization (90C26) Best approximation, Chebyshev systems (41A50) Eigenvalues, singular values, and eigenvectors (15A18) Multilinear algebra, tensor calculus (15A69)
Related Items (1)
TR-STF: a fast and accurate tensor ring decomposition algorithm via defined scaled tri-factorization
Cites Work
- Tensor Decompositions and Applications
- Tensor-Train Decomposition
- Factorization strategies for third-order tensors
- Tensor-tensor products with invertible linear transforms
- Generalized visual information analysis via tensorial algebra
- T-positive semidefiniteness of third-order symmetric tensors and T-semidefinite programming
- ST-SVD factorization and s-diagonal tensors
- Generalized tensor function via the tensor singular value decomposition based on the T-product
- Infinite and finite dimensional Hilbert tensors
- T-Jordan canonical form and T-Drazin inverse based on the T-product
- A New Truncation Strategy for the Higher-Order Singular Value Decomposition
- The tensor t‐function: A definition for functions of third‐order tensors
- A Multilinear Singular Value Decomposition
- Exact Tensor Completion Using t-SVD
- Tensor Decomposition for Signal Processing and Machine Learning
- An Order-$p$ Tensor Factorization with Applications in Imaging
- Third-Order Tensors as Operators on Matrices: A Theoretical and Computational Framework with Applications in Imaging
- Third-order tensors as linear operators on a space of matrices
This page was built for publication: Hot-SVD: higher order t-singular value decomposition for tensors based on tensor-tensor product
