Fiber Sampling Approach to Canonical Polyadic Decomposition and Application to Tensor Completion (Q5232122)

scientific article; zbMATH DE number 7099846

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English	Fiber Sampling Approach to Canonical Polyadic Decomposition and Application to Tensor Completion	scientific article; zbMATH DE number 7099846

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instance of

scholarly article

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title

Fiber Sampling Approach to Canonical Polyadic Decomposition and Application to Tensor Completion (English)

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zbMATH Open document ID

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SIAM Journal on Matrix Analysis and Applications

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publication date

29 August 2019

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Mathematics Subject Classification ID

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tensors

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polyadic decomposition

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describes a project that uses

TensorToolbox

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Tensorlab

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MaRDI profile type

MaRDI publication profile

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cites work

Graphs and matrices

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A Practical Randomized CP Tensor Decomposition

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Randomized interpolative decomposition of separated representations

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Analysis of individual differences in multidimensional scaling via an $n$-way generalization of ``Eckart-Young'' decomposition

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Exact matrix completion via convex optimization

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On Generic Identifiability of 3-Tensors of Small Rank

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A Link between the Canonical Decomposition in Multilinear Algebra and Simultaneous Matrix Diagonalization

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Decompositions of a Higher-Order Tensor in Block Terms—Part II: Definitions and Uniqueness

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On the Uniqueness of the Canonical Polyadic Decomposition of Third-Order Tensors---Part I: Basic Results and Uniqueness of One Factor Matrix

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On the Uniqueness of the Canonical Polyadic Decomposition of Third-Order Tensors---Part II: Uniqueness of the Overall Decomposition

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Canonical Polyadic Decomposition of Third-Order Tensors: Reduction to Generalized Eigenvalue Decomposition

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Generic Uniqueness Conditions for the Canonical Polyadic Decomposition and INDSCAL

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Canonical polyadic decomposition of third-order tensors: relaxed uniqueness conditions and algebraic algorithm

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A randomized algorithm for a tensor-based generalization of the singular value decomposition

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Fast monte-carlo algorithms for finding low-rank approximations

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Tensor completion and low-n-rank tensor recovery via convex optimization

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Q4917542

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Rank-one completions of partial matrices and completely rank-nonincreasing linear functionals

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Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions

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Q4902474

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Application of the three‐way decomposition for matrix compression

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Kruskal's Permutation Lemma and the Identification of CANDECOMP/PARAFAC and Bilinear Models with Constant Modulus Constraints

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Three-way arrays: rank and uniqueness of trilinear decompositions, with application to arithmetic complexity and statistics

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A Decomposition for Three-Way Arrays

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A Simpler Approach to Matrix Completion

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Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization

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Tensor Decomposition for Signal Processing and Machine Learning

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Coupled Canonical Polyadic Decompositions and (Coupled) Decompositions in Multilinear Rank-$(L_r,n,L_r,n,1)$ Terms---Part I: Uniqueness

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Coupled Canonical Polyadic Decompositions and (Coupled) Decompositions in Multilinear Rank- $(L_{r,n},L_{r,n},1)$ Terms---Part II: Algorithms

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Multidimensional Harmonic Retrieval via Coupled Canonical Polyadic Decomposition—Part I: Model and Identifiability

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Multidimensional Harmonic Retrieval via Coupled Canonical Polyadic Decomposition—Part II: Algorithm and Multirate Sampling

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Coupled Canonical Polyadic Decompositions and Multiple Shift Invariance in Array Processing

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Sufficient conditions for uniqueness in Candecomp/Parafac and Indscal with random component matrices

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On Kruskal's uniqueness condition for the Candecomp/Parafac decomposition

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Rank and optimal computation of generic tensors

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On tensor completion via nuclear norm minimization

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full work available at URL

https://doi.org/10.1137/17m1140790

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OpenAlex ID

W2965565138

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Sitelinks

Mathematics(1 entry)

mardi Publication:5232122