L
Lieven De Lathauwer
Researcher at Katholieke Universiteit Leuven
Publications - 250
Citations - 16461
Lieven De Lathauwer is an academic researcher from Katholieke Universiteit Leuven. The author has contributed to research in topics: Tensor & Tensor (intrinsic definition). The author has an hindex of 49, co-authored 240 publications receiving 14266 citations. Previous affiliations of Lieven De Lathauwer include Centre national de la recherche scientifique & University of Virginia.
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Journal ArticleDOI
A Multilinear Singular Value Decomposition
TL;DR: There is a strong analogy between several properties of the matrix and the higher-order tensor decomposition; uniqueness, link with the matrix eigenvalue decomposition, first-order perturbation effects, etc., are analyzed.
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On the Best Rank-1 and Rank-( R 1 , R 2 ,. . ., R N ) Approximation of Higher-Order Tensors
TL;DR: A multilinear generalization of the best rank-R approximation problem for matrices, namely, the approximation of a given higher-order tensor, in an optimal least-squares sense, by a tensor that has prespecified column rank value, rowRank value, etc.
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Tensor Decomposition for Signal Processing and Machine Learning
Nicholas D. Sidiropoulos,Lieven De Lathauwer,Xiao Fu,Kejun Huang,Evangelos E. Papalexakis,Christos Faloutsos +5 more
TL;DR: The material covered includes tensor rank and rank decomposition; basic tensor factorization models and their relationships and properties; broad coverage of algorithms ranging from alternating optimization to stochastic gradient; statistical performance analysis; and applications ranging from source separation to collaborative filtering, mixture and topic modeling, classification, and multilinear subspace learning.
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Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis
Andrzej Cichocki,Danilo P. Mandic,Lieven De Lathauwer,Guoxu Zhou,Qibin Zhao,Cesar F. Caiafa,Huy Anh Phan +6 more
TL;DR: Benefiting from the power of multilinear algebra as their mathematical backbone, data analysis techniques using tensor decompositions are shown to have great flexibility in the choice of constraints which match data properties and extract more general latent components in the data than matrix-based methods.
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A Link between the Canonical Decomposition in Multilinear Algebra and Simultaneous Matrix Diagonalization
TL;DR: This paper derives a new and relatively weak deterministic sufficient condition for uniqueness in the decomposition of higher-order tensors which have the property that the rank is smaller than the greatest dimension.