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ORCIDLieven De Lathauwer
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- affiliation: Catholic University of Leuven, Belgium
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2020 – today
- 2022
[j100]Stijn Hendrikx, Lieven De Lathauwer:
Block Row Kronecker-Structured Linear Systems With a Low-Rank Tensor Solution. Frontiers Appl. Math. Stat. 8: 832883 (2022)- [j99]Muzaffer Ayvaz, Lieven De Lathauwer:
CPD-Structured Multivariate Polynomial Optimization. Frontiers Appl. Math. Stat. 8: 836433 (2022) - [j98]Nithin Govindarajan, Nico Vervliet, Lieven De Lathauwer:
Regression and Classification With Spline-Based Separable Expansions. Frontiers Big Data 5: 688496 (2022) - [j97]Guillaume O. Berger, Pierre-Antoine Absil, Lieven De Lathauwer, Raphaël M. Jungers, Marc Van Barel:
Equivalent polyadic decompositions of matrix multiplication tensors. J. Comput. Appl. Math. 406: 113941 (2022) - [j96]Eric Evert, Michiel Vandecappelle, Lieven De Lathauwer:
A Recursive Eigenspace Computation for the Canonical Polyadic Decomposition. SIAM J. Matrix Anal. Appl. 43(1): 274-300 (2022) - [j95]Eric Evert, Lieven De Lathauwer:
Guarantees for Existence of a Best Canonical Polyadic Approximation of a Noisy Low-Rank Tensor. SIAM J. Matrix Anal. Appl. 43(1): 328-369 (2022) - [j94]Nithin Govindarajan, Ethan N. Epperly, Lieven De Lathauwer:
$(L_r, L_r, 1)$-Decompositions, Sparse Component Analysis, and the Blind Separation of Sums of Exponentials. SIAM J. Matrix Anal. Appl. 43(2): 912-938 (2022) - [j93]Michiel Vandecappelle
, Lieven De Lathauwer:
From multilinear SVD to multilinear UTV decomposition. Signal Process. 198: 108575 (2022) - [j92]Eric Evert, Michiel Vandecappelle, Lieven De Lathauwer:
Canonical Polyadic Decomposition via the Generalized Schur Decomposition. IEEE Signal Process. Lett. 29: 937-941 (2022) - [c71]Eric Evert, Michiel Vandecappelle, Lieven De Lathauwer:
CPD Computation via Recursive Eigenspace Decompositions. ICASSP 2022: 9067-9071 - [i11]Eric Evert, Michiel Vandecappelle, Lieven De Lathauwer:
Canonical Polyadic Decomposition via the generalized Schur decomposition. CoRR abs/2202.11414 (2022) - [i10]Pooya Ashtari, Diana Maria Sima, Lieven De Lathauwer, Dominique Sappey-Marinier, Frederik Maes, Sabine Van Huffel:
Factorizer: A Scalable Interpretable Approach to Context Modeling for Medical Image Segmentation. CoRR abs/2202.12295 (2022) - 2021
- [j91]Michiel Vandecappelle, Nico Vervliet, Lieven De Lathauwer:
Inexact Generalized Gauss-Newton for Scaling the Canonical Polyadic Decomposition With Non-Least-Squares Cost Functions. IEEE J. Sel. Top. Signal Process. 15(3): 491-505 (2021) - [j90]Ignat Domanov, Lieven De Lathauwer:
From Computation to Comparison of Tensor Decompositions. SIAM J. Matrix Anal. Appl. 42(2): 449-474 (2021) - [j89]Jeroen Vanderstukken, Lieven De Lathauwer:
Systems of Polynomial Equations, Higher-order Tensor Decompositions, and Multidimensional Harmonic Retrieval: A Unifying Framework. Part I: The Canonical Polyadic Decomposition. SIAM J. Matrix Anal. Appl. 42(2): 883-912 (2021) - [j88]Jeroen Vanderstukken, Patrick Kürschner, Ignat Domanov, Lieven De Lathauwer:
Systems of Polynomial Equations, Higher-Order Tensor Decompositions, and Multidimensional Harmonic Retrieval: A Unifying Framework. Part II: The Block Term Decomposition. SIAM J. Matrix Anal. Appl. 42(2): 913-953 (2021) - [c70]Cécile Hautecoeur, François Glineur, Lieven De Lathauwer:
Hierarchical alternating nonlinear least squares for nonnegative matrix factorization using rational functions. EUSIPCO 2021: 1045-1049 - [c69]Muzaffer Ayvaz, Lieven De Lathauwer:
Tensor-Based Multivariate Polynomial Optimization with Application in Blind Identification. EUSIPCO 2021: 1080-1084 - [i9]Eric Evert, Lieven De Lathauwer:
Guarantees for existence of a best canonical polyadic approximation of a noisy low-rank tensor. CoRR abs/2112.08283 (2021) - [i8]Eric Evert, Michiel Vandecappelle, Lieven De Lathauwer:
A recursive eigenspace computation for the Canonical Polyadic decomposition. CoRR abs/2112.08303 (2021) - 2020
- [j87]Ignat Domanov, Lieven De Lathauwer:
On Uniqueness and Computation of the Decomposition of a Tensor into Multilinear Rank-(1, Lr, Lr) Terms. SIAM J. Matrix Anal. Appl. 41(2): 747-803 (2020) - [j86]Xiao Fu, Nico Vervliet, Lieven De Lathauwer, Kejun Huang, Nicolas Gillis:
Computing Large-Scale Matrix and Tensor Decomposition With Structured Factors: A Unified Nonconvex Optimization Perspective. IEEE Signal Process. Mag. 37(5): 78-94 (2020) - [j85]Michiel Vandecappelle, Nico Vervliet, Lieven De Lathauwer:
A Second-Order Method for Fitting the Canonical Polyadic Decomposition With Non-Least-Squares Cost. IEEE Trans. Signal Process. 68: 4454-4465 (2020) - [c68]Nico Vervliet, Andreas Themelis, Panagiotis Patrinos, Lieven De Lathauwer:
A Quadratically Convergent Proximal Algorithm For Nonnegative Tensor Decomposition. EUSIPCO 2020: 1020-1024 - [i7]Christos Chatzichristos, Eleftherios Kofidis, Lieven De Lathauwer, Sergios Theodoridis, Sabine Van Huffel:
Early soft and flexible fusion of EEG and fMRI via tensor decompositions. CoRR abs/2005.07134 (2020) - [i6]Xiao Fu, Nico Vervliet, Lieven De Lathauwer, Kejun Huang, Nicolas Gillis:
Nonconvex Optimization Tools for Large-Scale Matrix and Tensor Decomposition with Structured Factors. CoRR abs/2006.08183 (2020)
2010 – 2019
- 2019
- [j84]Alwin Stegeman, Lieven De Lathauwer:
Rayleigh Quotient Methods for Estimating Common Roots of Noisy Univariate Polynomials. Comput. Methods Appl. Math. 19(1): 147-163 (2019) - [j83]Mikael Sørensen, Lieven De Lathauwer:
Fiber Sampling Approach to Canonical Polyadic Decomposition and Application to Tensor Completion. SIAM J. Matrix Anal. Appl. 40(3): 888-917 (2019) - [j82]Nico Vervliet, Otto Debals, Lieven De Lathauwer:
Exploiting Efficient Representations in Large-Scale Tensor Decompositions. SIAM J. Sci. Comput. 41(2): A789-A815 (2019) - [j81]Xiao-Feng Gong, Qiu-Hua Lin, Fengyu Cong, Lieven De Lathauwer:
Double coupled canonical polyadic decomposition of third-order tensors: Algebraic algorithm and relaxed uniqueness conditions. Signal Process. Image Commun. 73: 22-36 (2019) - [j80]Halandur Nagaraja Bharath, Otto Debals, Diana Maria Sima, Uwe Himmelreich, Lieven De Lathauwer, Sabine Van Huffel:
Tensor-Based Method for Residual Water Suppression in $^1$H Magnetic Resonance Spectroscopic Imaging. IEEE Trans. Biomed. Eng. 66(2): 584-594 (2019) - [c67]Michiel Vandecappelle, Lieven De Lathauwer:
Low Multilinear Rank Updating. ACSSC 2019: 437-441 - [c66]Stijn Hendrikx, Martijn Boussé, Nico Vervliet, Lieven De Lathauwer:
Algebraic and Optimization Based Algorithms for Multivariate Regression Using Symmetric Tensor Decomposition. CAMSAP 2019: 475-479 - [c65]Nico Vervliet, Michiel Vandecappelle, Martijn Boussé, Rob Zink, Lieven De Lathauwer:
Recent Numerical and Conceptual Advances for Tensor Decompositions - A Preview of Tensorlab 4.0. DSW 2019: 310-314 - [c64]Alexander Caicedo, Ofelie De Wel, Michiel Vandecappelle, Liesbeth Thewissen, Anne Smits, Karel Allegaert, Lieven De Lathauwer, Gunnar Naulaers, Sabine Van Huffel:
Monitoring of Brain Hemodynamics Coupling in Neonates using Updated Tensor Decompositions. EMBC 2019: 660-663 - [c63]Martijn Boussé, Nikos D. Sidiropoulos, Lieven De Lathauwer:
NLS Algorithm for Kronecker-Structured Linear Systems with a CPD Constrained Solution. EUSIPCO 2019: 1-5 - [c62]Simon Van Eyndhoven, Nico Vervliet, Lieven De Lathauwer, Sabine Van Huffel:
Identifying Stable Components of Matrix /Tensor Factorizations via Low-Rank Approximation of Inter-Factorization Similarity. EUSIPCO 2019: 1-5 - [c61]Michiel Vandecappelle, Nico Vervliet, Lieven De Lathauwer:
Rank-one Tensor Approximation with Beta-divergence Cost Functions. EUSIPCO 2019: 1-5 - [c60]Christos Chatzichristos, Michiel Vandecappelle, Eleftherios Kofidis, Sergios Theodoridis, Lieven De Lathauwer, Sabine Van Huffel:
Tensor-based Blind fMRI Source Separation Without the Gaussian Noise Assumption - A β-Divergence Approach. GlobalSIP 2019: 1-5 - [c59]Mikael Sørensen, Nicholas D. Sidiropoulos, Lieven De Lathauwer:
Canonical Polyadic Decomposition of a Tensor That Has Missing Fibers: A Monomial Factorization Approach. ICASSP 2019: 7490-7494 - [i5]Guillaume O. Berger, Pierre-Antoine Absil, Lieven De Lathauwer, Raphaël M. Jungers, Marc Van Barel:
Equivalent Polyadic Decompositions of Matrix Multiplication Tensors. CoRR abs/1902.03950 (2019) - [i4]Ignat Domanov, Lieven De Lathauwer:
From computation to comparison of tensor decompositions. CoRR abs/1912.04694 (2019) - 2018
- [j79]Martijn Bousse, Nico Vervliet, Ignat Domanov, Otto Debals, Lieven De Lathauwer:
Linear systems with a canonical polyadic decomposition constrained solution: Algorithms and applications. Numer. Linear Algebra Appl. 25(6) (2018) - [j78]Frederik Van Eeghem, Lieven De Lathauwer:
Algorithms for Canonical Polyadic Decomposition With Block-Circulant Factors. IEEE Signal Process. Lett. 25(6): 798-802 (2018) - [j77]Frederik Van Eeghem, Otto Debals, Lieven De Lathauwer:
Tensor Similarity in Two Modes. IEEE Trans. Signal Process. 66(5): 1273-1285 (2018) - [j76]Xiao-Feng Gong, Qiu-Hua Lin, Fengyu Cong, Lieven De Lathauwer:
Double Coupled Canonical Polyadic Decomposition for Joint Blind Source Separation. IEEE Trans. Signal Process. 66(13): 3475-3490 (2018) - [j75]Mikael Sørensen, Ignat Domanov, Lieven De Lathauwer:
Coupled Canonical Polyadic Decompositions and Multiple Shift Invariance in Array Processing. IEEE Trans. Signal Process. 66(14): 3665-3680 (2018) - [j74]Frederik Van Eeghem, Otto Debals, Nico Vervliet, Lieven De Lathauwer:
Coupled and Incomplete Tensors in Blind System Identification. IEEE Trans. Signal Process. 66(23): 6137-6147 (2018) - [c58]Simon Geirnaert, Griet Goovaerts, Sibasankar Padhy, Martijn Bousse, Lieven De Lathauwer, Sabine Van Huffel:
Tensor-basedECG Signal Processing Applied to Atrial Fibrillation Detection. ACSSC 2018: 799-805 - [c57]Guillaume Olikier, Pierre-Antoine Absil, Lieven De Lathauwer:
A variable projection method for block term decomposition of higher-order tensors. ESANN 2018 - [c56]Martijn Bousse, Lieven De Lathauwer:
Large-Scale Autoregressive System Identification Using Kronecker Product Equations. GlobalSIP 2018: 1348-1352 - [c55]Guillaume Olikier, Pierre-Antoine Absil, Lieven De Lathauwer:
Variable Projection Applied to Block Term Decomposition of Higher-Order Tensors. LVA/ICA 2018: 139-148 - [c54]Michiel Vandecappelle, Martijn Bousse, Nico Vervliet, Lieven De Lathauwer:
CPD Updating Using Low-Rank Weights. ICASSP 2018: 6368-6372 - [c53]Simon Van Eyndhoven, Martijn Bousse, Borbála Hunyadi, Lieven De Lathauwer, Sabine Van Huffel:
Single-channel EEG Classification by Multi-channel Tensor subspace Learning and Regression. MLSP 2018: 1-6 - 2017
- [j73]Steven Delrue, Vladislav Aleshin, Mikael Sørensen, Lieven De Lathauwer:
Simulation Study of the Localization of a Near-Surface Crack Using an Air-Coupled Ultrasonic Sensor Array. Sensors 17(4): 930 (2017) - [j72]Ignat Domanov, Alwin Stegeman, Lieven De Lathauwer:
On the Largest Multilinear Singular Values of Higher-Order Tensors. SIAM J. Matrix Anal. Appl. 38(4): 1434-1453 (2017) - [j71]Otto Debals, Marc Van Barel, Lieven De Lathauwer:
Nonnegative Matrix Factorization Using Nonnegative Polynomial Approximations. IEEE Signal Process. Lett. 24(7): 948-952 (2017) - [j70]Halandur Nagaraja Bharath, Diana Maria Sima, Nicolas Sauwen, Uwe Himmelreich, Lieven De Lathauwer, Sabine Van Huffel:
Nonnegative Canonical Polyadic Decomposition for Tissue-Type Differentiation in Gliomas. IEEE J. Biomed. Health Informatics 21(4): 1124-1132 (2017) - [j69]Martijn Bousse, Otto Debals, Lieven De Lathauwer:
A Tensor-Based Method for Large-Scale Blind Source Separation Using Segmentation. IEEE Trans. Signal Process. 65(2): 346-358 (2017) - [j68]Mikael Sørensen, Lieven De Lathauwer:
Multidimensional Harmonic Retrieval via Coupled Canonical Polyadic Decomposition - Part I: Model and Identifiability. IEEE Trans. Signal Process. 65(2): 517-527 (2017) - [j67]Mikael Sørensen, Lieven De Lathauwer:
Multidimensional Harmonic Retrieval via Coupled Canonical Polyadic Decomposition - Part II: Algorithm and Multirate Sampling. IEEE Trans. Signal Process. 65(2): 528-539 (2017) - [j66]Nicholas D. Sidiropoulos, Lieven De Lathauwer, Xiao Fu, Kejun Huang, Evangelos E. Papalexakis, Christos Faloutsos:
Tensor Decomposition for Signal Processing and Machine Learning. IEEE Trans. Signal Process. 65(13): 3551-3582 (2017) - [j65]Frederik Van Eeghem, Mikael Sørensen, Lieven De Lathauwer:
Tensor Decompositions With Several Block-Hankel Factors and Application in Blind System Identification. IEEE Trans. Signal Process. 65(15): 4090-4101 (2017) - [j64]Mikael Sørensen, Frederik Van Eeghem, Lieven De Lathauwer:
Blind Multichannel Deconvolution and Convolutive Extensions of Canonical Polyadic and Block Term Decompositions. IEEE Trans. Signal Process. 65(15): 4132-4145 (2017) - [j63]Martijn Bousse, Otto Debals, Lieven De Lathauwer:
Tensor-Based Large-Scale Blind System Identification Using Segmentation. IEEE Trans. Signal Process. 65(21): 5770-5784 (2017) - [c52]Lieven De Lathauwer, Eleftherios Kofidis:
Coupled matrix-tensor factorizations - The case of partially shared factors. ACSSC 2017: 711-715 - [c51]Martijn Bousse, Lieven De Lathauwer:
Nonlinear least squares algorithm for canonical polyadic decomposition using low-rank weights. CAMSAP 2017: 1-5 - [c50]Martijn Bousse, Nico Vervliet, Otto Debals, Lieven De Lathauwer:
Face recognition as a kronecker product equation. CAMSAP 2017: 1-5 - [c49]Martijn Bousse, Griet Goovaerts, Nico Vervliet, Otto Debals, Sabine Van Huffel, Lieven De Lathauwer:
Irregular heartbeat classification using Kronecker Product Equations. EMBC 2017: 438-441 - [c48]Simon Van Eyndhoven, Borbála Hunyadi, Lieven De Lathauwer, Sabine Van Huffel:
Flexible fusion of electroencephalography and functional magnetic resonance imaging: Revealing neural-hemodynamic coupling through structured matrix-tensor factorization. EUSIPCO 2017: 26-30 - [c47]Michiel Vandecappelle, Nico Vervliet, Lieven De Lathauwer:
Nonlinear least squares updating of the canonical polyadic decomposition. EUSIPCO 2017: 663-667 - [c46]Frederik Van Eeghem, Lieven De Lathauwer:
Second-order tensor-based convolutive ICA: Deconvolution versus tensorization. ICASSP 2017: 2252-2256 - 2016
- [j62]Laurent Sorber, Ignat Domanov, Marc Van Barel, Lieven De Lathauwer:
Exact line and plane search for tensor optimization. Comput. Optim. Appl. 63(1): 121-142 (2016) - [j61]Nico Vervliet, Lieven De Lathauwer:
A Randomized Block Sampling Approach to Canonical Polyadic Decomposition of Large-Scale Tensors. IEEE J. Sel. Top. Signal Process. 10(2): 284-295 (2016) - [j60]Ignat Domanov, Lieven De Lathauwer:
Generic Uniqueness of a Structured Matrix Factorization and Applications in Blind Source Separation. IEEE J. Sel. Top. Signal Process. 10(4): 701-711 (2016) - [j59]Otto Debals, Marc Van Barel, Lieven De Lathauwer:
Löwner-Based Blind Signal Separation of Rational Functions With Applications. IEEE Trans. Signal Process. 64(8): 1909-1918 (2016) - [j58]Mikael Sørensen, Lieven De Lathauwer:
Multiple Invariance ESPRIT for Nonuniform Linear Arrays: A Coupled Canonical Polyadic Decomposition Approach. IEEE Trans. Signal Process. 64(14): 3693-3704 (2016) - [c45]Nico Vervliet, Otto Debals, Lieven De Lathauwer:
Tensorlab 3.0 - Numerical optimization strategies for large-scale constrained and coupled matrix/tensor factorization. ACSSC 2016: 1733-1738 - [c44]Halandur Nagaraja Bharath, Nicolas Sauwen, Diana Maria Sima, Uwe Himmelreich, Lieven De Lathauwer, Sabine Van Huffel:
Canonical polyadic decomposition for tissue type differentiation using multi-parametric MRI in high-grade gliomas. EUSIPCO 2016: 547-551 - [c43]Martijn Bousse, Otto Debals, Lieven De Lathauwer:
A tensor-based method for large-scale blind system identification using segmentation. EUSIPCO 2016: 2015-2019 - [c42]Xiao-Feng Gong, Qiu-Hua Lin, Otto Debals, Nico Vervliet, Lieven De Lathauwer:
Coupled rank-(Lm, Ln, •) block term decomposition by coupled block simultaneous generalized Schur decomposition. ICASSP 2016: 2554-2558 - [c41]Mikael Sørensen, Lieven De Lathauwer:
Shift invariance, incomplete arrays and coupled CPD: A case study. SAM 2016: 1-5 - [i3]Nicholas D. Sidiropoulos, Lieven De Lathauwer, Xiao Fu, Kejun Huang, Evangelos E. Papalexakis, Christos Faloutsos:
Tensor Decomposition for Signal Processing and Machine Learning. CoRR abs/1607.01668 (2016) - 2015
- [j57]Laurent Sorber, Marc Van Barel, Lieven De Lathauwer:
Structured Data Fusion. IEEE J. Sel. Top. Signal Process. 9(4): 586-600 (2015) - [j56]Mikael Sørensen, Lieven De Lathauwer:
Coupled Canonical Polyadic Decompositions and (Coupled) Decompositions in Multilinear Rank-(Lr, n, Lr, n, 1) Terms - Part I: Uniqueness. SIAM J. Matrix Anal. Appl. 36(2): 496-522 (2015) - [j55]Mikael Sørensen, Ignat Domanov, Lieven De Lathauwer:
Coupled Canonical Polyadic Decompositions and (Coupled) Decompositions in Multilinear Rank- (Lr, n, Lr, n, 1) Terms - Part II: Algorithms. SIAM J. Matrix Anal. Appl. 36(3): 1015-1045 (2015) - [j54]Mikael Sørensen, Lieven De Lathauwer:
New Uniqueness Conditions for the Canonical Polyadic Decomposition of Third-Order Tensors. SIAM J. Matrix Anal. Appl. 36(4): 1381-1403 (2015) - [j53]Ignat Domanov, Lieven De Lathauwer:
Generic Uniqueness Conditions for the Canonical Polyadic Decomposition and INDSCAL. SIAM J. Matrix Anal. Appl. 36(4): 1567-1589 (2015) - [j52]Andrzej Cichocki, Danilo P. Mandic, Lieven De Lathauwer, Guoxu Zhou, Qibin Zhao, Cesar F. Caiafa, Anh Huy Phan:
Tensor Decompositions for Signal Processing Applications: From two-way to multiway component analysis. IEEE Signal Process. Mag. 32(2): 145-163 (2015) - [c40]Halandur Nagaraja Bharath, Diana Maria Sima, Nicolas Sauwen, Uwe Himmelreich, Lieven De Lathauwer, Sabine Van Huffel:
Tensor based tumor tissue type differentiation using magnetic resonance spectroscopic imaging. EMBC 2015: 7003-7006 - [c39]Martijn Bousse, Otto Debals, Lieven De Lathauwer:
A novel deterministic method for large-scale blind source separation. EUSIPCO 2015: 1890-1894 - [c38]Otto Debals, Lieven De Lathauwer:
Stochastic and Deterministic Tensorization for Blind Signal Separation. LVA/ICA 2015: 3-13 - [c37]Philippe Dreesen, Thomas Goossens, Mariya Ishteva, Lieven De Lathauwer, Johan Schoukens:
Block-Decoupling Multivariate Polynomials Using the Tensor Block-Term Decomposition. LVA/ICA 2015: 14-21 - [c36]Otto Debals, Marc Van Barel, Lieven De Lathauwer:
Blind signal separation of rational functions using Löwner-based tensorization. ICASSP 2015: 4145-4149 - 2014
- [j51]Borbála Hunyadi, Daan Camps, Laurent Sorber, Wim Van Paesschen, Maarten De Vos, Sabine Van Huffel, Lieven De Lathauwer:
Block term decomposition for modelling epileptic seizures. EURASIP J. Adv. Signal Process. 2014: 139 (2014) - [j50]Dario A. Bini, Lieven De Lathauwer, Nicola Mastronardi, Marc Van Barel, Raf Vandebril, Paul Van Dooren:
Introduction to the special issue. J. Comput. Appl. Math. 272: 275 (2014) - [j49]Marco Signoretto, Dinh Quoc Tran, Lieven De Lathauwer, Johan A. K. Suykens:
Learning with tensors: a framework based on convex optimization and spectral regularization. Mach. Learn. 94(3): 303-351 (2014) - [j48]Ignat Domanov, Lieven De Lathauwer:
Canonical Polyadic Decomposition of Third-Order Tensors: Reduction to Generalized Eigenvalue Decomposition. SIAM J. Matrix Anal. Appl. 35(2): 636-660 (2014) - [j47]Laurent Sorber, Marc Van Barel, Lieven De Lathauwer:
Numerical Solution of Bivariate and Polyanalytic Polynomial Systems. SIAM J. Numer. Anal. 52(4): 1551-1572 (2014) - [j46]Nico Vervliet, Otto Debals, Laurent Sorber, Lieven De Lathauwer:
Breaking the Curse of Dimensionality Using Decompositions of Incomplete Tensors: Tensor-based scientific computing in big data analysis. IEEE Signal Process. Mag. 31(5): 71-79 (2014) - [c35]Mikael Sørensen, Lieven De Lathauwer:
Multidimensional ESPRIT: A coupled canonical polyadic decomposition approach. SAM 2014: 441-444 - [i2]Andrzej Cichocki, Danilo P. Mandic, Anh Huy Phan, Cesar F. Caiafa, Guoxu Zhou, Qibin Zhao, Lieven De Lathauwer:
Tensor Decompositions for Signal Processing Applications From Two-way to Multiway Component Analysis. CoRR abs/1403.4462 (2014) - 2013
- [j45]Laurent Sorber, Marc Van Barel, Lieven De Lathauwer:
Optimization-Based Algorithms for Tensor Decompositions: Canonical Polyadic Decomposition, Decomposition in Rank-(Lr, Lr, 1) Terms, and a New Generalization. SIAM J. Optim. 23(2): 695-720 (2013) - [j44]Ignat Domanov, Lieven De Lathauwer:
On the Uniqueness of the Canonical Polyadic Decomposition of Third-Order Tensors - Part I: Basic Results and Uniqueness of One Factor Matrix. SIAM J. Matrix Anal. Appl. 34(3): 855-875 (2013) - [j43]Ignat Domanov, Lieven De Lathauwer:
On the Uniqueness of the Canonical Polyadic Decomposition of Third-Order Tensors - Part II: Uniqueness of the Overall Decomposition. SIAM J. Matrix Anal. Appl. 34(3): 876-903 (2013) - [j42]Mikael Sørensen, Lieven De Lathauwer:
Blind Signal Separation via Tensor Decomposition With Vandermonde Factor: Canonical Polyadic Decomposition. IEEE Trans. Signal Process. 61(22): 5507-5519 (2013) - [c34]Mikael Sørensen, Lieven De Lathauwer:
Coupled tensor decompositions for applications in array signal processing. CAMSAP 2013: 228-231 - [i1]Marco Signoretto, Lieven De Lathauwer, Johan A. K. Suykens:
Learning Tensors in Reproducing Kernel Hilbert Spaces with Multilinear Spectral Penalties. CoRR abs/1310.4977 (2013) - 2012
- [j41]Laurent Sorber, Marc Van Barel, Lieven De Lathauwer:
Unconstrained Optimization of Real Functions in Complex Variables. SIAM J. Optim. 22(3): 879-898 (2012) - [j40]Mikael Sørensen, Lieven De Lathauwer, Pierre Comon, Sylvie Icart, Luc Deneire:
Canonical Polyadic Decomposition with a Columnwise Orthonormal Factor Matrix. SIAM J. Matrix Anal. Appl. 33(4): 1190-1213 (2012) - [j39]Mikael Sørensen, Lieven De Lathauwer, Sylvie Icart, Luc Deneire:
On Jacobi-type methods for blind equalization of paraunitary channels. Signal Process. 92(3): 617-624 (2012) - [j38]Maarten De Vos, Dimitri Nion, Sabine Van Huffel, Lieven De Lathauwer:
A combination of parallel factor and independent component analysis. Signal Process. 92(12): 2990-2999 (2012) - [j37]Marco Signoretto, Emanuele Olivetti, Lieven De Lathauwer, Johan A. K. Suykens:
Classification of Multichannel Signals With Cumulant-Based Kernels. IEEE Trans. Signal Process. 60(5): 2304-2314 (2012) - [c33]Mikael Sørensen, Lieven De Lathauwer:
Tensor decompositions with Vandermonde factor and applications in signal processing. ACSCC 2012: 890-894 - [c32]Lieven De Lathauwer:
Block Component Analysis, a New Concept for Blind Source Separation. LVA/ICA 2012: 1-8 - 2011
- [j36]Marco Signoretto, Lieven De Lathauwer, Johan A. K. Suykens:
A kernel-based framework to tensorial data analysis. Neural Networks 24(8): 861-874 (2011) - [j35]Mariya Ishteva, Pierre-Antoine Absil, Sabine Van Huffel, Lieven De Lathauwer:
Best Low Multilinear Rank Approximation of Higher-Order Tensors, Based on the Riemannian Trust-Region Scheme. SIAM J. Matrix Anal. Appl. 32(1): 115-135 (2011) - [j34]Lieven De Lathauwer:
Blind Separation of Exponential Polynomials and the Decomposition of a Tensor in Rank-(Lr, Lr, 1) Terms. SIAM J. Matrix Anal. Appl. 32(4): 1451-1474 (2011) - [j33]Ahmad Karfoul, Laurent Albera, Lieven De Lathauwer:
Iterative methods for the canonical decomposition of multi-way arrays: Application to blind underdetermined mixture identification. Signal Process. 91(8): 1789-1802 (2011) - [j32]