DOI10.1137/060661685zbMath1177.15031OpenAlexW1989881099WikidataQ124986643 ScholiaQ124986643MaRDI QIDQ5320723
Lieven De Lathauwer
Publication date: 22 July 2009
Published in: SIAM Journal on Matrix Analysis and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1137/060661685
$(L_r,L_r,1)$-Decompositions, Sparse Component Analysis, and the Blind Separation of Sums of Exponentials ⋮
Dimension of Marginals of Kronecker Product Models ⋮
The average number of critical rank-one approximations to a tensor ⋮
An efficient randomized fixed-precision algorithm for tensor singular value decomposition ⋮
An Algebraic Approach to Nonorthogonal General Joint Block Diagonalization ⋮
Provable stochastic algorithm for large-scale fully-connected tensor network decomposition ⋮
Face Representations via Tensorfaces of Various Complexities ⋮
A note on the three-way generalization of the Jordan canonical form ⋮
Solvability of the periodic problem for higher-order linear functional differential equations ⋮
On partial and generic uniqueness of block term tensor decompositions ⋮
Perturbation analysis for matrix joint block diagonalization ⋮
Successive unconstrained dual optimization method for~rank-one approximation to tensors ⋮
Exact line and plane search for tensor optimization ⋮
Structuring data with block term decomposition: decomposition of joint tensors and variational block term decomposition as a parametrized mixture distribution model ⋮
On identifiability of higher order block term tensor decompositions of rank Lr⊗ rank-1 ⋮
A Barzilai-Borwein gradient algorithm for spatio-temporal Internet traffic data completion via tensor triple decomposition ⋮
Differential-geometric Newton method for the best rank-\((R _{1}, R _{2}, R _{3})\) approximation of tensors ⋮
Jordan canonical form of three-way tensor with multilinear rank \((4,4,3)\) ⋮
Systems of Polynomial Equations, Higher-Order Tensor Decompositions, and Multidimensional Harmonic Retrieval: A Unifying Framework. Part II: The Block Term Decomposition ⋮
Decoupling Multivariate Polynomials Using First-Order Information and Tensor Decompositions ⋮
Hyperspectral Super-resolution Accounting for Spectral Variability: Coupled Tensor LL1-Based Recovery and Blind Unmixing of the Unknown Super-resolution Image
This page was built for publication: Decompositions of a Higher-Order Tensor in Block Terms—Part I: Lemmas for Partitioned Matrices
