L. Lathauwer

Simon Van Eyndhoven, Sabine Van Huffel, Lieven De Lathauwer, 2018, 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP).

A Geometric Newton Method for Oja's Vector Field

Sabine Van Huffel, Lieven De Lathauwer, Pierre-Antoine Absil, 2008, Neural Computation.

Tensor Decompositions With Several Block-Hankel Factors and Application in Blind System Identification

Lieven De Lathauwer, Mikael Sorensen, Frederik Van Eeghem, 2017, IEEE Transactions on Signal Processing.

Stochastic and Deterministic Tensorization for Blind Signal Separation

Lieven De Lathauwer, Otto Debals, L. Lathauwer, 2015, LVA/ICA.

Block term decomposition for modelling epileptic seizures

L. Lathauwer, S. Huffel, M. Vos, 2014, EURASIP J. Adv. Signal Process..

Breaking the Curse of Dimensionality Using Decompositions of Incomplete Tensors: Tensor-based scientific computing in big data analysis

Nico Vervliet, Lieven De Lathauwer, Laurent Sorber, 2014, IEEE Signal Processing Magazine.

A Combination of Parallel Factor and Independent Component Analysis

Sabine Van Huffel, Maarten De Vos, Lieven De Lathauwer, 2022 .

Canonical Polyadic Decomposition with a Columnwise Orthonormal Factor Matrix

Pierre Comon, Lieven De Lathauwer, Luc Deneire, 2012, SIAM J. Matrix Anal. Appl..

Block Component Analysis, a New Concept for Blind Source Separation

Lieven De Lathauwer, L. Lathauwer, L. D. Lathauwer, 2012, LVA/ICA.

A short introduction to tensor-based methods for Factor Analysis and Blind Source Separation

Lieven De Lathauwer, L. Lathauwer, L. D. Lathauwer, 2011, 2011 7th International Symposium on Image and Signal Processing and Analysis (ISPA).

Spatially constrained ICA algorithm with an application in EEG processing

Sabine Van Huffel, Maarten De Vos, Lieven De Lathauwer, 2011, Signal Process..

Hybrid Clustering of Multiple Information Sources via HOSVD

Bart De Moor, Lieven De Lathauwer, Xinhai Liu, 2010, ISNN.

Algebraic methods after prewhitening

L. De Lathauwer, L. Lathauwer, L. D. Lathauwer, 2010 .

Separation of multiple scatterers in NEWS experiments using Independent Component Analysis (ICA)

Dimitri Nion, L. De Lathauwer, Sigfried Vanaverbeke, 2010 .

A survey of tensor methods

Lieven De Lathauwer, L. Lathauwer, L. D. Lathauwer, 2009, 2009 IEEE International Symposium on Circuits and Systems.

An efficient Jacobi-type algorithm for blind equalization of paraunitary channels

Lieven De Lathauwer, Luc Deneire, Mikael Sørensen, 2009, 2009 17th European Signal Processing Conference.

A Block Component Model-Based Blind DS-CDMA Receiver

Lieven De Lathauwer, Dimitri Nion, L. Lathauwer, 2008, IEEE Transactions on Signal Processing.

A Method to Avoid Diverging Components in the Candecomp/Parafac Model for Generic I˟J˟2 Arrays

Lieven De Lathauwer, Alwin Stegeman, L. Lathauwer, 2008, SIAM J. Matrix Anal. Appl..

Decompositions of a Higher-Order Tensor in Block Terms - Part II: Definitions and Uniqueness

Lieven De Lathauwer, L. Lathauwer, L. D. Lathauwer, 2008, SIAM J. Matrix Anal. Appl..

Algorithm for imposing SOBI-type constraints on the CP model

Sabine Van Huffel, Maarten De Vos, Lieven De Lathauwer, 2008, 2008 IEEE International Symposium on Circuits and Systems.

Blind Identification of Underdetermined Mixtures by Simultaneous Matrix Diagonalization

Lieven De Lathauwer, Joséphine Castaing, L. Lathauwer, 2008, IEEE Transactions on Signal Processing.

Differential-geometric Newton method for the best rank-(R1, R2, R3) approximation of tensors

Sabine Van Huffel, Lieven De Lathauwer, Pierre-Antoine Absil, 2008, Numerical Algorithms.

Fourth-Order Cumulant-Based Blind Identification of Underdetermined Mixtures

Lieven De Lathauwer, Jean-François Cardoso, Joséphine Castaing, 2007, IEEE Transactions on Signal Processing.

A Link between the Canonical Decomposition in Multilinear Algebra and Simultaneous Matrix Diagonalization

Lieven De Lathauwer, L. Lathauwer, L. D. Lathauwer, 2006, SIAM J. Matrix Anal. Appl..

A prewhitening-induced bound on the identification error in independent component analysis

Joos Vandewalle, Bart De Moor, Lieven De Lathauwer, 2005, IEEE Transactions on Circuits and Systems I: Regular Papers.

Parallel factor analysis by means of simultaneous matrix decompositions

L. De Lathauwer, L. Lathauwer, L. D. Lathauwer, 2005 .

Computation of the Canonical Decomposition by Means of a Simultaneous Generalized Schur Decomposition

Joos Vandewalle, Bart De Moor, Lieven De Lathauwer, 2005, SIAM J. Matrix Anal. Appl..

Thin QR and SVD factorizations for simultaneous blind signal extraction

Sergio Cruces, Andrzej Cichocki, Lieven De Lathauwer, 2004, 2004 12th European Signal Processing Conference.

The generalized higher order singular value decomposition and the oriented signal-to-signal ratios of pairs of signal tensors and their use in signal processing

Joos Vandewalle, Pierre Comon, Lieven De Lathauwer, 2003 .

On the blind separation of non-circular sources

Bart De Moor, Lieven De Lathauwer, L. Lathauwer, 2002, 2002 11th European Signal Processing Conference.

Independent component analysis and (simultaneous) third-order tensor diagonalization

Joos Vandewalle, Bart De Moor, Lieven De Lathauwer, 2001, IEEE Trans. Signal Process..

SVD-based methodologies for fetal electrocardiogram extraction

Joos Vandewalle, Bart De Moor, Lieven De Lathauwer, 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

Fetal electrocardiogram extraction by blind source subspace separation

Joos Vandewalle, Bart De Moor, Lieven De Lathauwer, 2000, IEEE Transactions on Biomedical Engineering.

An algebraic approach to the blind identification of paraunitary filters

Joos Vandewalle, Bart De Moor, Lieven De Lathauwer, 2000, 2000 IEEE Wireless Communications and Networking Conference. Conference Record (Cat. No.00TH8540).

A residual bound for the mixing matrix in ICA

Joos Vandewalle, Lieven De Lathauwer, L. Lathauwer, 1998, 9th European Signal Processing Conference (EUSIPCO 1998).

The application of higher order singular value decomposition to independent component analysis

L. Lathauwer, B. Moor, J. Vandewalle, 1995 .

Monitoring of Brain Hemodynamics Coupling in Neonates using Updated Tensor Decompositions

A Caicedo, O De Wel, M Vandecappelle, 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Classification of Multichannel Signals With Cumulant-Based Kernels

Johan A. K. Suykens, Marco Signoretto, Lieven De Lathauwer, 2012, IEEE Transactions on Signal Processing.

A Randomized Block Sampling Approach to Canonical Polyadic Decomposition of Large-Scale Tensors

Nico Vervliet, Lieven De Lathauwer, L. Lathauwer, 2016, IEEE Journal of Selected Topics in Signal Processing.

Best Low Multilinear Rank Approximation of Higher-Order Tensors, Based on the Riemannian Trust-Region Scheme

Sabine Van Huffel, Lieven De Lathauwer, Pierre-Antoine Absil, 2011, SIAM J. Matrix Anal. Appl..

Learning Tensors in Reproducing Kernel Hilbert Spaces with Multilinear Spectral Penalties pdf

Johan A. K. Suykens, Lieven De Lathauwer, Marco Signoretto, 2013, ArXiv.

Numerical Solution of Bivariate and Polyanalytic Polynomial Systems

Lieven De Lathauwer, Laurent Sorber, Marc Van Barel, 2014, SIAM J. Numer. Anal..

Simultaneous Matrix Diagonalization : the Overcomplete Case

Lieven De Lathauwer, L. Lathauwer, 2003 .

Learning with tensors: a framework based on convex optimization and spectral regularization

Johan A. K. Suykens, Lieven De Lathauwer, Dinh Quoc Tran, 2014, Machine Learning.

Tensor Decomposition for Signal Processing and Machine Learning

Nikos D. Sidiropoulos, Christos Faloutsos, Lieven De Lathauwer, 2016, IEEE Transactions on Signal Processing.

Unsupervised tissue segmentation of MRSI data using Canonical Correlation Analysis

Sabine Van Huffel, Paul Van Hecke, Lieven De Lathauwer, 2005 .

Best multilinear rank approximation of tensors : a trust-region algorithm and local mimina (Best poster award)

Sabine Van Huffel, Lieven De Lathauwer, Pierre-Antoine Absil, 2009 .

A Second-Order Method for Fitting the Canonical Polyadic Decomposition With Non-Least-Squares Cost

Nico Vervliet, Michiel Vandecappelle, Lieven De Lathauwer, 2020, IEEE Transactions on Signal Processing.

Guarantees for existence of a best canonical polyadic approximation of a noisy low-rank tensor pdf

Lieven De Lathauwer, Eric Evert, L. Lathauwer, 2021, SIAM J. Matrix Anal. Appl..

Structured Data Fusion

Lieven De Lathauwer, Laurent Sorber, Marc Van Barel, 2015, IEEE Journal of Selected Topics in Signal Processing.

Optimization-Based Algorithms for Tensor Decompositions: Canonical Polyadic Decomposition, Decomposition in Rank-(Lr, Lr, 1) Terms, and a New Generalization

Lieven De Lathauwer, Laurent Sorber, Marc Van Barel, 2013, SIAM J. Optim..

On the Uniqueness of the Canonical Polyadic Decomposition of Third-Order Tensors - Part I: Basic Results and Uniqueness of One Factor Matrix

Lieven De Lathauwer, Ignat Domanov, L. Lathauwer, 2013, SIAM J. Matrix Anal. Appl..

CPD-Structured Multivariate Polynomial Optimization

L. Lathauwer, Muzaffer Ayvaz, L. D. Lathauwer, 2022, Frontiers in Applied Mathematics and Statistics.

Block Row Kronecker-Structured Linear Systems With a Low-Rank Tensor Solution

L. Lathauwer, Stijn Hendrikx, L. D. Lathauwer, 2022, Frontiers in Applied Mathematics and Statistics.

A recursive eigenspace computation for the Canonical Polyadic decomposition pdf

Lieven De Lathauwer, Michiel Vandecappelle, Eric Evert, 2021, SIAM J. Matrix Anal. Appl..

Inexact Generalized Gauss–Newton for Scaling the Canonical Polyadic Decomposition With Non-Least-Squares Cost Functions

L. Lathauwer, Nico Vervliet, Michiel Vandecappelle, 2021, IEEE Journal of Selected Topics in Signal Processing.

Bilinear factorizations subject to monomial equality constraints via tensor decompositions

Lieven De Lathauwer, Mikael Sorensen, N. D. Sidiropoulos, 2021, Linear Algebra and its Applications.

Tensor-based Blind fMRI Source Separation Without the Gaussian Noise Assumption — A β-Divergence Approach

Sergios Theodoridis, Michiel Vandecappelle, Lieven De Lathauwer, 2019, 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP).

Fiber Sampling Approach to Canonical Polyadic Decomposition and Application to Tensor Completion

Lieven De Lathauwer, Mikael Sørensen, L. Lathauwer, 2019, SIAM J. Matrix Anal. Appl..

Canonical Polyadic Decomposition of a Tensor That Has Missing Fibers: A Monomial Factorization Approach

Lieven De Lathauwer, Nicholas D. Sidiropoulos, Mikael Sorensen, 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Numerical Optimization-Based Algorithms for Data Fusion

Nico Vervliet, L. De Lathauwer, L. Lathauwer, 2019, Data Handling in Science and Technology.

Linear systems with a canonical polyadic decomposition constrained solution: Algorithms and applications

Nico Vervliet, Lieven De Lathauwer, Otto Debals, 2018, Numer. Linear Algebra Appl..

Algorithms for Canonical Polyadic Decomposition With Block-Circulant Factors

Frederik Van Eeghem, L. Lathauwer, L. D. Lathauwer, 2018, IEEE Signal Processing Letters.

A Tensor-Based Method for Large-Scale Blind Source Separation Using Segmentation

Lieven De Lathauwer, Otto Debals, Martijn Bousse, 2017, IEEE Transactions on Signal Processing.

Shift invariance, incomplete arrays and coupled CPD: A case study

Lieven De Lathauwer, Mikael Sorensen, L. Lathauwer, 2016, 2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM).

A novel deterministic method for large-scale blind source separation

Lieven De Lathauwer, Otto Debals, Martijn Bousse, 2015, 2015 23rd European Signal Processing Conference (EUSIPCO).

Coupled Canonical Polyadic Decompositions and (Coupled) Decompositions in Multilinear Rank-(Lr, n, Lr, n, 1) Terms - Part I: Uniqueness

Lieven De Lathauwer, Mikael Sørensen, L. Lathauwer, 2015, SIAM J. Matrix Anal. Appl..

Generic Uniqueness Conditions for the Canonical Polyadic Decomposition and INDSCAL

Lieven De Lathauwer, Ignat Domanov, L. Lathauwer, 2014, SIAM J. Matrix Anal. Appl..

From tensor to coupled matrix/tensor decomposition

Lieven De Lathauwer, Laurent Sorber, Marc Van Barel, 2014 .

Canonical Polyadic Decomposition of Third-Order Tensors: Reduction to Generalized Eigenvalue Decomposition

Lieven De Lathauwer, Ignat Domanov, L. Lathauwer, 2013, SIAM J. Matrix Anal. Appl..

Coupled tensor decompositions for applications in array signal processing

Lieven De Lathauwer, Mikael Sørensen, L. Lathauwer, 2013, 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP).

Blind Signal Separation via Tensor Decomposition With Vandermonde Factor: Canonical Polyadic Decomposition

Lieven De Lathauwer, Mikael Sørensen, L. Lathauwer, 2013, IEEE Transactions on Signal Processing.

On the Uniqueness of the Canonical Polyadic Decomposition of Third-Order Tensors - Part II: Uniqueness of the Overall Decomposition

Lieven De Lathauwer, Ignat Domanov, L. Lathauwer, 2013, SIAM J. Matrix Anal. Appl..

Tensor decompositions with block-Toeplitz structure and applications in signal processing

Lieven De Lathauwer, Mikael Sørensen, L. Lathauwer, 2011, 2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR).

Iterative methods for the canonical decomposition of multi-way arrays: Application to blind underdetermined mixture identification

Laurent Albera, Lieven De Lathauwer, Ahmad Karfoul, 2011, Signal Process..

Decompositions of a Higher-Order Tensor in Block Terms - Part I: Lemmas for Partitioned Matrices

Lieven De Lathauwer, L. Lathauwer, L. D. Lathauwer, 2008, SIAM J. Matrix Anal. Appl..

Decompositions of a Higher-Order Tensor in Block Terms - Part III: Alternating Least Squares Algorithms

Lieven De Lathauwer, Dimitri Nion, L. Lathauwer, 2008, SIAM J. Matrix Anal. Appl..

Blind Deconvolution of DS-CDMA Signals by Means of Decomposition in Rank-$(1,L,L)$ Terms

Lieven De Lathauwer, Alexandre de Baynast, L. Lathauwer, 2008, IEEE Transactions on Signal Processing.

Swamp reducing technique for tensor decomposition

Lieven De Lathauwer, Stefan Kindermann, Carmeliza Navasca, 2008, 2008 16th European Signal Processing Conference.

Canonical Decomposition of Ictal Scalp EEG and Accurate Source Localisation: Principles and Simulation Study

Wim Van Paesschen, Sabine Van Huffel, Maarten De Vos, 2007, Comput. Intell. Neurosci..

Imposing Independence Constraints in the CP Model

Sabine Van Huffel, Maarten De Vos, Lieven De Lathauwer, 2007, ICA.

Tensor-based techniques for the blind separation of DS-CDMA signals

Lieven De Lathauwer, Joséphine Castaing, L. Lathauwer, 2007, Signal Process..

ResearchArticle Canonical Decomposition of Ictal Scalp EEG and Accurate Source Localisation: Principles and Simulation Study

Sabine Van Huffel, Maarten De Vos, Bart Vanrumste, 2007 .

Second-Order Blind Identification of Underdetermined Mixtures

Lieven De Lathauwer, Joséphine Castaing, L. Lathauwer, 2006, ICA.

Exponential data fitting using multilinear algebra: the single‐channel and multi‐channel case

Sabine Van Huffel, Lieven De Lathauwer, Jean-Michel Papy, 2005, Numer. Linear Algebra Appl..

An algebraic technique for the blind separation of DS-CDMA signals

Lieven De Lathauwer, Joséphine Castaing, L. Lathauwer, 2004, 2004 12th European Signal Processing Conference.

The canonical decomposition and blind identification with more inputs than outputs : Some algebraic results

Lieven De Lathauwer, L. Lathauwer, L. D. Lathauwer, 2003 .

Löwner-Based Blind Signal Separation of Rational Functions With Applications

Lieven De Lathauwer, Marc Van Barel, Otto Debals, 2016, IEEE Transactions on Signal Processing.

An enhanced line search scheme for complex-valued tensor decompositions. Application in DS-CDMA

Lieven De Lathauwer, Dimitri Nion, L. Lathauwer, 2008, Signal Process..

The use of total least squares data fitting in the shape-from-moments problem

Sabine Van Huffel, Lieven De Lathauwer, Philippe Lemmerling, 2006, Signal Process..

Common pole estimation in multi-channel exponential data modeling

Sabine Van Huffel, Lieven De Lathauwer, Jean-Michel Papy, 2006, Signal Process..

Tensorlab 3.0 — Numerical optimization strategies for large-scale constrained and coupled matrix/tensor factorization

Nico Vervliet, Lieven De Lathauwer, Otto Debals, 2016, 2016 50th Asilomar Conference on Signals, Systems and Computers.

Nonlinear least squares algorithm for canonical polyadic decomposition using low-rank weights

Lieven De Lathauwer, Martijn Bousse, L. Lathauwer, 2017, 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP).

A Quadratically Convergent Proximal Algorithm For Nonnegative Tensor Decomposition

L. Lathauwer, Panagiotis Patrinos, Andreas Themelis, 2020, 2020 28th European Signal Processing Conference (EUSIPCO).

NLS Algorithm for Kronecker-Structured Linear Systems with a CPD Constrained Solution

Lieven De Lathauwer, Martijn Boussé, Nikos Sidiropoulos, 2019, 2019 27th European Signal Processing Conference (EUSIPCO).

The Power of Tensor-Based Approaches in Cardiac Applications

Sabine Van Huffel, Sibasankar Padhy, Lieven De Lathauwer, 2019, Series in BioEngineering.

CPD Updating Using Low-Rank Weights

Nico Vervliet, Lieven De Lathauwer, Michiel Vandecappelle, 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Face recognition as a kronecker product equation

Nico Vervliet, Lieven De Lathauwer, Otto Debals, 2017, 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP).

Coupled matrix-tensor factorizations — The case of partially shared factors

Lieven De Lathauwer, Eleftherios Kofidis, L. Lathauwer, 2017, 2017 51st Asilomar Conference on Signals, Systems, and Computers.

Irregular heartbeat classification using Kronecker Product Equations

Nico Vervliet, Sabine Van Huffel, Lieven De Lathauwer, 2017, 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

On the Largest Multilinear Singular Values of Higher-Order Tensors

Lieven De Lathauwer, Ignat Domanov, Alwin Stegeman, 2016, SIAM J. Matrix Anal. Appl..

Higher Order Tensor-Based Method for Delayed Exponential Fitting

Lieven De Lathauwer, Rémy Boyer, Karim Abed-Meraim, 2007, IEEE Transactions on Signal Processing.

Delayed exponential fitting by best tensor rank-(R/sub 1/, R/sub 2/, R/sub 3/) approximation

Lieven De Lathauwer, Karim Abed-Meraim, Rémy Boyer, 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

Dimensionality Reduction in ICA and Rank-(R1, R2, ..., RN) Reduction in Multilinear Algebra

Joos Vandewalle, Lieven De Lathauwer, L. Lathauwer, 2004, ICA.

A Grassmann-Rayleigh Quotient Iteration for Dimensionality Reduction in ICA

Joos Vandewalle, Lieven De Lathauwer, Luc Hoegaerts, 2004, ICA.

Algebraic techniques for the blind deconvolution of Constant Modulus signals

Lieven De Lathauwer, L. Lathauwer, 2004, 2004 12th European Signal Processing Conference.

A kernel-based framework to tensorial data analysis

Johan A. K. Suykens, Lieven De Lathauwer, Marco Signoretto, 2011, Neural Networks.

Improved non-parametric sparse recovery with data matched penalties

Johan A. K. Suykens, Lieven De Lathauwer, Kristiaan Pelckmans, 2010, 2010 2nd International Workshop on Cognitive Information Processing.

A tensor-based method for large-scale blind system identification using segmentation

Lieven De Lathauwer, Otto Debals, Martijn Bousse, 2016, 2016 24th European Signal Processing Conference (EUSIPCO).

BLIND MIMO PARA-UNITARY EQUALIZATION

Lieven De Lathauwer, Ludwig Rota, L. Lathauwer, 2004 .

Early soft and flexible fusion of EEG and fMRI via tensor decompositions pdf

Sabine Van Huffel, Lieven De Lathauwer, Sergios Theodoridis, 2020, ArXiv.

Identifying Stable Components of Matrix /Tensor Factorizations via Low-Rank Approximation of Inter-Factorization Similarity

Simon Van Eyndhoven, Nico Vervliet, Sabine Van Huffel, 2019, 2019 27th European Signal Processing Conference (EUSIPCO).

Parafac with orthogonality in one mode and applications in DS-CDMA systems

Luc Deneire, Lieven De Lathauwer, Mikael Sørensen, 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

New Uniqueness Conditions for the Canonical Polyadic Decomposition of Third-Order Tensors

Lieven De Lathauwer, Mikael Sørensen, L. Lathauwer, 2015, SIAM J. Matrix Anal. Appl..

Nonconvex Optimization Tools for Large-Scale Matrix and Tensor Decomposition with Structured Factors pdf

Nico Vervliet, Nicolas Gillis, Xiao Fu, 2020, ArXiv.

Equivalent Polyadic Decompositions of Matrix Multiplication Tensors pdf

Raphaël M. Jungers, Lieven De Lathauwer, Guillaume O. Berger, 2019, J. Comput. Appl. Math..

Unconstrained Optimization of Real Functions in Complex Variables

Lieven De Lathauwer, Laurent Sorber, Marc Van Barel, 2012, SIAM J. Optim..

Blind Multichannel Deconvolution and Convolutive Extensions of Canonical Polyadic and Block Term Decompositions

Lieven De Lathauwer, Mikael Sørensen, Frederik Van Eeghem, 2017, IEEE Transactions on Signal Processing.

Multidimensional Harmonic Retrieval via Coupled Canonical Polyadic Decomposition—Part I: Model and Identifiability

Lieven De Lathauwer, Mikael Sørensen, L. Lathauwer, 2017, IEEE Transactions on Signal Processing.

On Jacobi-type methods for blind equalization of paraunitary channels

Lieven De Lathauwer, Luc Deneire, Mikael Sørensen, 2012, Signal Process..

On Uniqueness and Computation of the Decomposition of a Tensor into Multilinear Rank-(1, Lr, Lr) Terms

Lieven De Lathauwer, Ignat Domanov, L. Lathauwer, 2018, SIAM J. Matrix Anal. Appl..

Generic Uniqueness of a Structured Matrix Factorization and Applications in Blind Source Separation

Lieven De Lathauwer, Ignat Domanov, L. Lathauwer, 2016, IEEE Journal of Selected Topics in Signal Processing.

Recent Numerical and Conceptual Advances for Tensor Decompositions — A Preview of Tensorlab 4.0

Nico Vervliet, Lieven De Lathauwer, Michiel Vandecappelle, 2019, 2019 IEEE Data Science Workshop (DSW).

Blind Separation of Exponential Polynomials and the Decomposition of a Tensor in Rank-(Lr, Lr, 1) Terms

Lieven De Lathauwer, L. Lathauwer, L. D. Lathauwer, 2011, SIAM J. Matrix Anal. Appl..

Flexible fusion of electroencephalography and functional magnetic resonance imaging: Revealing neural-hemodynamic coupling through structured matrix-tensor factorization

Simon Van Eyndhoven, Sabine Van Huffel, Lieven De Lathauwer, 2017, 2017 25th European Signal Processing Conference (EUSIPCO).

Coupled Canonical Polyadic Decompositions and (Coupled) Decompositions in Multilinear Rank- (Lr, n, Lr, n, 1) Terms - Part II: Algorithms

Lieven De Lathauwer, Mikael Sørensen, Ignat Domanov, 2015, SIAM J. Matrix Anal. Appl..

From computation to comparison of tensor decompositions pdf

Lieven De Lathauwer, Ignat Domanov, L. Lathauwer, 2019, SIAM J. Matrix Anal. Appl..

Comparison of the performance of matrix and tensor based multi-channel harmonic analysis

L. Lathauwer, S. Huffel, Mariya Ishteva, 2006 .

Complex Variables

L. Lathauwer, M. Barel, Laurent Sorber, 2011 .

Double coupled canonical polyadic decomposition of third-order tensors: Algebraic algorithm and relaxed uniqueness conditions

Fengyu Cong, Lieven De Lathauwer, Qiu-Hua Lin, 2019, Signal Process. Image Commun..

Multidimensional Harmonic Retrieval via Coupled Canonical Polyadic Decomposition—Part II: Algorithm and Multirate Sampling

Lieven De Lathauwer, Mikael Sørensen, L. Lathauwer, 2017, IEEE Transactions on Signal Processing.

Multiple Invariance ESPRIT for Nonuniform Linear Arrays: A Coupled Canonical Polyadic Decomposition Approach

Lieven De Lathauwer, Mikael Sørensen, L. Lathauwer, 2016, IEEE Transactions on Signal Processing.

Hierarchical alternating nonlinear least squares for nonnegative matrix factorization using rational functions

Lieven De Lathauwer, François Glineur, Cécile Hautecoeur, 2021, 2021 29th European Signal Processing Conference (EUSIPCO).

Nonnegative Matrix Factorization Using Nonnegative Polynomial Approximations

Lieven De Lathauwer, Marc Van Barel, Otto Debals, 2017, IEEE Signal Processing Letters.