Practical Leverage-Based Sampling for Low-Rank Tensor Decomposition

DOI10.1137/21M1441754OpenAlexW3039567237WikidataQ114073980 ScholiaQ114073980MaRDI QIDQ5099421

Brett W. Larsen, Tamara G. Kolda

Publication date: 31 August 2022

Published in: SIAM Journal on Matrix Analysis and Applications (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2006.16438

zbMATH Keywords

tensor decompositionmatrix sketchingrandomized numerical linear algebracanonical polyadicleverage score samplingRandNLA

Mathematics Subject Classification ID

Multilinear algebra, tensor calculus (15A69) Numerical linear algebra (65F99) Numerical methods for low-rank matrix approximation; matrix compression (65F55)

Related Items (2)

A Higher-Order Generalized Singular Value Decomposition for Rank-Deficient Matrices ⋮ A block-randomized stochastic method with importance sampling for CP tensor decomposition

Uses Software

• TensorToolbox
• FROSTT

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