The Sup-norm Perturbation of HOSVD and Low Rank Tensor Denoising
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Publication:5381114
zbMath1489.62169arXiv1707.01207MaRDI QIDQ5381114
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Publication date: 7 June 2019
Full work available at URL: https://arxiv.org/abs/1707.01207
Estimation in multivariate analysis (62H12) Factorization of matrices (15A23) Multilinear algebra, tensor calculus (15A69)
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Cites Work
- Unnamed Item
- Unnamed Item
- Tensor Decompositions and Applications
- Fast community detection by SCORE
- Concentration inequalities and moment bounds for sample covariance operators
- Asymptotics and concentration bounds for bilinear forms of spectral projectors of sample covariance
- Delocalization of eigenvectors of random matrices with independent entries
- High-dimensional classification using features annealed independence rules
- Rate-optimal perturbation bounds for singular subspaces with applications to high-dimensional statistics
- On polynomial time methods for exact low-rank tensor completion
- The two-to-infinity norm and singular subspace geometry with applications to high-dimensional statistics
- Computational barriers in minimax submatrix detection
- Entrywise bounds for eigenvectors of random graphs
- Computational and statistical boundaries for submatrix localization in a large noisy matrix
- Tensor decompositions for learning latent variable models
- Perturbation of Linear Forms of Singular Vectors Under Gaussian Noise
- Random weighted projections, random quadratic forms and random eigenvectors
- Random Tensors and Planted Cliques
- On the Tensor SVD and the Optimal Low Rank Orthogonal Approximation of Tensors
- A Multilinear Singular Value Decomposition
- On the Best Rank-1 and Rank-(R1 ,R2 ,. . .,RN) Approximation of Higher-Order Tensors
- An $\ell_{\infty}$ Eigenvector Perturbation Bound and Its Application to Robust Covariance Estimation
- Tensor SVD: Statistical and Computational Limits
- Singular Vector Perturbation Under Gaussian Noise
- Most Tensor Problems Are NP-Hard
- The Rotation of Eigenvectors by a Perturbation. III
- Perturbation bounds in connection with singular value decomposition
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