On optimal low rank Tucker approximation for tensors: the case for an adjustable core size

DOI10.1007/s10898-014-0231-xzbMath1330.90083OpenAlexW2094845866MaRDI QIDQ496606

Zhening Li, Bilian Chen, Shu-Zhong Zhang

Publication date: 22 September 2015

Published in: Journal of Global Optimization (Search for Journal in Brave)

Full work available at URL: https://researchportal.port.ac.uk/portal/en/publications/on-optimal-low-rank-tucker-approximation-for-tensors(67a33c97-930c-4b51-b1a3-49d9eec6fa55).html

zbMATH Keywords

low-rank approximationTucker decompositionmaximum block improvementmultiway array

Mathematics Subject Classification ID

Nonconvex programming, global optimization (90C26)

Uses Software

• JAFFE
• TensorToolbox

Cites Work

• Tensor Decompositions and Applications
• Differential-geometric Newton method for the best rank-\((R _{1}, R _{2}, R _{3})\) approximation of tensors
• Principal component analysis of three-mode data by means of alternating least squares algorithms
• A singular value decomposition of a \(k\)-way array for a principal component analysis of multiway data, \(\text{PTA-}k\)
• An approach to n-mode components analysis
• Analysis of individual differences in multidimensional scaling via an \(n\)-way generalization of ``Eckart-Young decomposition
• Rank-One Approximation to High Order Tensors
• On the Best Rank-1 Approximation of Higher-Order Supersymmetric Tensors
• Maximum Block Improvement and Polynomial Optimization
• Local Convergence of the Alternating Least Squares Algorithm for Canonical Tensor Approximation
• On Convergence of the Maximum Block Improvement Method
• The Best Rank-One Approximation Ratio of a Tensor Space
• On the successive supersymmetric rank-1 decomposition of higher-order supersymmetric tensors
• A Newton–Grassmann Method for Computing the Best Multilinear Rank-$(r_1,$ $r_2,$ $r_3)$ Approximation of a Tensor
• A Multilinear Singular Value Decomposition
• On the Best Rank-1 and Rank-(R₁ ,R₂ ,. . .,R_N) Approximation of Higher-Order Tensors
• Ambiguity Function Shaping for Cognitive Radar Via Complex Quartic Optimization
• Decompositions of a Higher-Order Tensor in Block Terms—Part III: Alternating Least Squares Algorithms
• Unnamed Item

This page was built for publication: On optimal low rank Tucker approximation for tensors: the case for an adjustable core size