Fiber Sampling Approach to Canonical Polyadic Decomposition and Application to Tensor Completion
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Publication:5232122
DOI10.1137/17M1140790zbMath1458.15045OpenAlexW2965565138MaRDI QIDQ5232122
Mikael Sørensen, Lieven De Lathauwer
Publication date: 29 August 2019
Published in: SIAM Journal on Matrix Analysis and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1137/17m1140790
Numerical optimization and variational techniques (65K10) Multilinear algebra, tensor calculus (15A69) Matrix completion problems (15A83)
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Cites Work
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- Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
- On tensor completion via nuclear norm minimization
- Canonical polyadic decomposition of third-order tensors: relaxed uniqueness conditions and algebraic algorithm
- Randomized interpolative decomposition of separated representations
- On Kruskal's uniqueness condition for the Candecomp/Parafac decomposition
- A randomized algorithm for a tensor-based generalization of the singular value decomposition
- Rank and optimal computation of generic tensors
- Three-way arrays: rank and uniqueness of trilinear decompositions, with application to arithmetic complexity and statistics
- Sufficient conditions for uniqueness in Candecomp/Parafac and Indscal with random component matrices
- Analysis of individual differences in multidimensional scaling via an \(n\)-way generalization of ``Eckart-Young decomposition
- Exact matrix completion via convex optimization
- On the Uniqueness of the Canonical Polyadic Decomposition of Third-Order Tensors---Part I: Basic Results and Uniqueness of One Factor Matrix
- On the Uniqueness of the Canonical Polyadic Decomposition of Third-Order Tensors---Part II: Uniqueness of the Overall Decomposition
- Canonical Polyadic Decomposition of Third-Order Tensors: Reduction to Generalized Eigenvalue Decomposition
- Tensor completion and low-n-rank tensor recovery via convex optimization
- A Decomposition for Three-Way Arrays
- Generic Uniqueness Conditions for the Canonical Polyadic Decomposition and INDSCAL
- Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization
- Multidimensional Harmonic Retrieval via Coupled Canonical Polyadic Decomposition—Part I: Model and Identifiability
- Multidimensional Harmonic Retrieval via Coupled Canonical Polyadic Decomposition—Part II: Algorithm and Multirate Sampling
- Coupled Canonical Polyadic Decompositions and Multiple Shift Invariance in Array Processing
- Tensor Decomposition for Signal Processing and Machine Learning
- A Practical Randomized CP Tensor Decomposition
- On Generic Identifiability of 3-Tensors of Small Rank
- Coupled Canonical Polyadic Decompositions and (Coupled) Decompositions in Multilinear Rank-$(L_r,n,L_r,n,1)$ Terms---Part I: Uniqueness
- Rank-one completions of partial matrices and completely rank-nonincreasing linear functionals
- A Link between the Canonical Decomposition in Multilinear Algebra and Simultaneous Matrix Diagonalization
- Application of the three‐way decomposition for matrix compression
- Decompositions of a Higher-Order Tensor in Block Terms—Part II: Definitions and Uniqueness
- Kruskal's Permutation Lemma and the Identification of CANDECOMP/PARAFAC and Bilinear Models with Constant Modulus Constraints
- A Simpler Approach to Matrix Completion
- Fast monte-carlo algorithms for finding low-rank approximations
- Coupled Canonical Polyadic Decompositions and (Coupled) Decompositions in Multilinear Rank- $(L_{r,n},L_{r,n},1)$ Terms---Part II: Algorithms
- Graphs and matrices
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