L
Laurent Sorber
Researcher at Katholieke Universiteit Leuven
Publications - 19
Citations - 990
Laurent Sorber is an academic researcher from Katholieke Universiteit Leuven. The author has contributed to research in topics: Tensor (intrinsic definition) & Tensor. The author has an hindex of 11, co-authored 19 publications receiving 855 citations. Previous affiliations of Laurent Sorber include University of Copenhagen Faculty of Science.
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Optimization-Based Algorithms for Tensor Decompositions: Canonical Polyadic Decomposition, Decomposition in Rank-$(L_r,L_r,1)$ Terms, and a New Generalization
TL;DR: Combined with an effective preconditioner, numerical experiments confirm that these methods are among the most efficient and robust currently available for computing the CPD, rank-$(L_r,L-r,1)$ BTD, and their generalized decomposition.
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Structured Data Fusion
TL;DR: The versatility of the SDF framework is demonstrated by means of four diverse applications, which are all solved entirely within Tensorlab's DSL.
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Unconstrained Optimization of Real Functions in Complex Variables
TL;DR: Although little known, it is possible to construct an expansion of the objective function in its original complex variables by notching up the real and imaginary parts of its complex argument.
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Breaking the Curse of Dimensionality Using Decompositions of Incomplete Tensors: Tensor-based scientific computing in big data analysis
TL;DR: In this article, a tensor can be represented by a decomposition, and this hypothesized structure can be exploited by using compressed sensing (CS) methods working on incomplete tensors, i.e., tensors with only a few known elements.
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Block term decomposition for modelling epileptic seizures
Borbála Hunyadi,Daan Camps,Laurent Sorber,Laurent Sorber,Wim Van Paesschen,Maarten De Vos,Sabine Van Huffel,Lieven De Lathauwer +7 more
TL;DR: Block term decomposition (BTD) is presented, allowing to model more variability in the data than what would be possible with CPD, and various real EEG recordings where BTD outperforms CPD in capturing complex seizure characteristics are shown.