Iterative hard thresholding for low CP-rank tensor models
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Publication:6042723
DOI10.1080/03081087.2021.1992335zbMath1514.65052arXiv1908.08479OpenAlexW3208057011MaRDI QIDQ6042723
Deanna Needell, Anna Ma, Rachel Grotheer, Shuang Li, Jing Qin
Publication date: 3 May 2023
Published in: Linear and Multilinear Algebra (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1908.08479
Multilinear algebra, tensor calculus (15A69) Numerical methods for low-rank matrix approximation; matrix compression (65F55)
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Cites Work
- Unnamed Item
- Tensor Decompositions and Applications
- A mathematical introduction to compressive sensing
- Uniqueness conditions for low-rank matrix recovery
- Factorization strategies for third-order tensors
- Iterative hard thresholding for compressed sensing
- From quantum to classical molecular dynamics: Reduced models and numerical analysis.
- Greedy low-rank approximation in Tucker format of solutions of tensor linear systems
- Low rank tensor recovery via iterative hard thresholding
- Analysis of individual differences in multidimensional scaling via an \(n\)-way generalization of ``Eckart-Young decomposition
- Exact matrix completion via convex optimization
- Normalized Iterative Hard Thresholding for Matrix Completion
- Tensor decompositions for learning latent variable models
- Compressive Multiplexing of Correlated Signals
- Robust principal component analysis?
- Decoding by Linear Programming
- Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
- A Multilinear Singular Value Decomposition
- Linear Convergence of Stochastic Iterative Greedy Algorithms With Sparse Constraints
- Efficient Tensor Completion for Color Image and Video Recovery: Low-Rank Tensor Train
- Relative Error Tensor Low Rank Approximation
- Tight Oracle Inequalities for Low-Rank Matrix Recovery From a Minimal Number of Noisy Random Measurements
- Most Tensor Problems Are NP-Hard
- Invertibility of symmetric random matrices