Subtracting a best rank‐1 approximation from p × p × 2(p≥2) tensors

DOI10.1002/nla.780zbMath1274.15002OpenAlexW2167602604MaRDI QIDQ4922263

Publication date: 29 May 2013

Published in: Numerical Linear Algebra with Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/nla.780

zbMATH Keywords

tensor decomposition tensor rank low rank approximation singular value decomposition (SVD)Candecomp Parafac best-fitting tensor decomposition multi-way component analysis

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Multilinear algebra, tensor calculus (15A69) Vector spaces, linear dependence, rank, lineability (15A03) Matrix pencils (15A22)

Related Items (2)

Structured multi-way arrays and their applications ⋮ On the Uniqueness and Perturbation to the Best Rank-One Approximation of a Tensor

Cites Work

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A Method to Avoid Diverging Components in the Candecomp/Parafac Model for Generic $I\timesJ\times2$ Arrays
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A Note on the Spectra and Pseudospectra of Waveform Relaxation Operators for Linear Differential-Algebraic Equations
Improving convergence performance of relaxation-based transient analysis by matrix splitting in circuit simulation
Low-Rank Approximation of Generic $p \timesq \times2$ Arrays and Diverging Components in the Candecomp/Parafac Model
Tensor Rank and the Ill-Posedness of the Best Low-Rank Approximation Problem
Subtracting a best rank-1 approximation may increase tensor rank

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