Singular vectors of orthogonally decomposable tensors
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Publication:4603780
DOI10.1080/03081087.2016.1277508zbMath1390.13083arXiv1603.09004OpenAlexW2963641987MaRDI QIDQ4603780
Anna Leah Seigal, Elina Robeva
Publication date: 19 February 2018
Published in: Linear and Multilinear Algebra (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1603.09004
Eigenvalues, singular values, and eigenvectors (15A18) Multilinear algebra, tensor calculus (15A69) Applications of commutative algebra (e.g., to statistics, control theory, optimization, etc.) (13P25)
Related Items (10)
Convergence rate analysis for the higher order power method in best rank one approximations of tensors ⋮ Robust Eigenvectors of Symmetric Tensors ⋮ Orthogonal decomposition of tensor trains ⋮ Discrete Fourier transform tensors and their eigenvalues ⋮ On the spectral problem for trivariate functions ⋮ The set of orthogonal tensor trains ⋮ A class of diameter six trees exhibiting graceful labeling ⋮ Alternating Mahalanobis Distance Minimization for Accurate and Well-Conditioned CP Decomposition ⋮ Tensor Ring Decomposition: Optimization Landscape and One-loop Convergence of Alternating Least Squares ⋮ Nondegeneracy of eigenvectors and singular vector tuples of tensors
Cites Work
- The number of singular vector tuples and uniqueness of best rank-one approximation of tensors
- Orthogonal and unitary tensor decomposition from an algebraic perspective
- Binomial ideals
- The number of eigenvalues of a tensor
- Secant varieties of Segre-Veronese varieties
- Orthogonal Tensor Decompositions
- Rank-One Approximation to High Order Tensors
- Tensor decompositions for learning latent variable models
- A Counterexample to the Possibility of an Extension of the Eckart--Young Low-Rank Approximation Theorem for the Orthogonal Rank Tensor Decomposition
- A Multilinear Singular Value Decomposition
- Most Tensor Problems Are NP-Hard
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