Reproduce experiments

Easily reproduce results, figures and tables from the publications of Lieven De Lathauwer's research group.

Apply algorithms

Apply our clearly documented algorithms to your own problems, guided by tutorials and demos.

Complements Tensorlab

All code in this repository is built on top of Tensorlab, which can be downloaded here.

Read about our philosophy

The rationale behind our reproducible research approach is explained in more detail in this journal paper.

Download the full repository (39.8 MB), consisting of the code for 44 publications, by filling out this form. Note that large files from certain papers are excluded from the full repository.* Individual papers can be downloaded below. All code in Tensorlab⁺ requires Tensorlab.

This repository can be cited as:
S. Hendrikx, M. Boussé, N. Vervliet, M. Vandecappelle, R. Kenis, and L. De Lathauwer, Tensorlab⁺, Available online, Version of Dec 2022 downloaded from https://www.tensorlabplus.net.

*Large files have been left out from the following papers:

These files can be obtained by following the links above and downloading the code for the individual papers.

Download code for individual papers

2022

From multilinear SVD to multilinear UTV decomposition

Michiel Vandecappelle, Lieven De Lathauwer
Updating the multilinear UTV decomposition

Michiel Vandecappelle, Lieven De Lathauwer
Towards more realistic simulations of microstructural evolution in oxidic systems

Inge Bellemans, Nico Vervliet, Lieven De Lathauwer, Nele Moelans, Kim Verbeken
Block row Kronecker-structured linear systems with a low-rank tensor solution

Stijn Hendrikx, Lieven De Lathauwer
Canonical polyadic decomposition via the generalized Schur decomposition

Eric Evert, Michiel Vandecappelle, Lieven De Lathauwer
Guarantees for existence of a best canonical polyadic approximation of a noisy low-rank tensor

Eric Evert, Lieven De Lathauwer
Regression and classification with spline-based separable expansions

Nithin Govindarajan, Nico Vervliet, Lieven De Lathauwer
A recursive eigenspace computation for the canonical polyadic decomposition

Eric Evert, Michiel Vandecappelle, Lieven De Lathauwer

2021

Numerical methods for the computation of tensor decompositions

Michiel Vandecappelle
Bilinear factorizations subject to monomial equality constraints via tensor decompositions

Mikael Sørensen, Lieven De Lathauwer, Nicholaos D. Sidiropoulos
Inexact generalized Gauss–Newton for scaling the canonical polyadic decomposition with non-least-squares cost functions

Michiel Vandecappelle, Nico Vervliet, Lieven De Lathauwer
From computation to comparison of tensor decompositions

Ignat Domanov, Lieven De Lathauwer
A quadratically convergent proximal algorithm for nonnegative tensor decomposition

Nico Vervliet, Andreas Themelis, Panos Patrinos, Lieven De Lathauwer

2020

A second-order method for fitting the canonical polyadic decomposition with non-least-squares cost

Michiel Vandecappelle, Nico Vervliet, Lieven De Lathauwer
On uniqueness and computation of the decomposition of a tensor into multilinear rank-$(1, L_r, L_r)$ terms

Ignat Domanov, Lieven De Lathauwer
Combining thermodynamics with tensor completion techniques to enable multicomponent microstructure prediction

Yuri Amorim Coutinho, Nico Vervliet, Lieven De Lathauwer, Nele Moelans

2019

Algebraic and optimization based algorithms for multivariate regression using symmetric tensor decomposition

Stijn Hendrikx, Martijn Boussé, Nico Vervliet, Lieven De Lathauwer
Fiber sampling approach to canonical polyadic decomposition and application to tensor completion

Mikael Sørensen, Lieven De Lathauwer
Exploiting efficient representations in large-scale tensor decompositions

Nico Vervliet, Lieven De Lathauwer
Numerical optimization-based algorithms for data fusion

Nico Vervliet, Lieven De Lathauwer

2018

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

Martijn Boussé, Nico Vervliet, Ignat Domanov, Otto Debals, Lieven De Lathauwer
Coupled canonical polyadic decompositions and multiple shift invariance in array processing

Mikael Sørensen, Ignat Domanov, Lieven De Lathauwer
Nonlinear least squares algorithm for canonical polyadic decomposition using low-rank weights

Martijn Boussé, Lieven De Lathauwer

2017

Face Recognition as a Kronecker product equation

Martijn Boussé, Nico Vervliet, Otto Debals, Lieven De Lathauwer
Tensor-based large-scale blind system identification using segmentation

Martijn Boussé, Otto Debals, Lieven De Lathauwer
Tensor decompositions with several block-Hankel factors and application in blind system identification

Frederik Van Eeghem, Mikael Sørensen, Lieven De Lathauwer
Nonlinear least squares updating of the canonical polyadic decomposition

Michiel Vandecappelle, Nico Vervliet, Lieven De Lathauwer
Blind multichannel deconvolution and convolutive extensions of canonical polyadic and block term decompositions

Mikael Sørensen, Frederik Van Eeghem, Lieven De Lathauwer
Nonnegative matrix factorization using nonnegative polynomial approximations

Otto Debals, Marc Van Barel, Lieven De Lathauwer
Irregular heartbeat classification using Kronecker product equations

Martijn Boussé, Griet Goovaerts, Nico Vervliet, Otto Debals, Sabine Van Huffel, Lieven De Lathauwer
Multidimensional harmonic retrieval via coupled canonical polyadic decomposition — Part II: Algorithm and multirate sampling

Mikael Sørensen, Lieven De Lathauwer
Canonical polyadic decomposition of third-order tensors: Relaxed uniqueness conditions and algebraic algorithm

Ignat Domanov, Lieven De Lathauwer
A tensor-based method for large-scale blind source separation using segmentation

Martijn Boussé, Otto Debals, Lieven De Lathauwer

2016

Multiple invariance ESPRIT for nonuniform linear arrays: A coupled canonical polyadic decomposition approach

Mikael Sørensen, Lieven De Lathauwer
Löwner-based blind signal separation of rational functions with applications

O. Debals, M. Van Barel, L. De Lathauwer
A randomized block sampling approach to canonical polyadic decomposition of large-scale tensors

Nico Vervliet, Lieven De Lathauwer

2015

New uniqueness conditions for the canonical polyadic decomposition of third-order tensors

Mikael Sørensen, Lieven De Lathauwer
Coupled canonical polyadic decompositions and (coupled) decompositions in multilinear rank-$(L_{r,n}, L_{r,n}, 1)$ terms — Part II: Algorithms

Mikael Sørensen, Lieven De Lathauwer

2014	Breaking the curse of dimensionality using decompositions of incomplete tensors: Tensor-based scientiﬁc computing in big data analysis Nico Vervliet, Otto Debals, Laurent Sorber, Lieven De Lathauwer

2013	Blind signal separation via tensor decomposition with Vandermonde factor: Canonical polyadic decomposition Mikael Sørensen, Lieven De Lathauwer

2012	Canonical polyadic decomposition with a columnwise orthonormal factor matrix Mikael Sørensen, Lieven De Lathauwer, Pierre Comon, Sylvie Icart, Luc Deneire

2008	Blind identification of underdetermined mixtures by simultaneous matrix diagonalization Lieven De Lathauwer, Joséphine Castaing Decompositions of a higher-order tensor in block terms — Part III: Alternating least squares algorithms Lieven De Lathauwer, Dimitri Nion

2007	Fourth-order cumulant-based blind identification of underdetermined mixtures Lieven De Lathauwer, Joséphine Castaing, Jean-François Cardoso

News

Dec 14, 2022: The second release adds the code for 10 new papers

The second release of Tensorlab⁺ provides support for 10 additional papers. The new code is available through both download types. In total, Tensorlab⁺ now allows one to recreate the results of 220+ experiments that are discussed in 44 papers, thus allowing the reproduction of 250+ figures and tables, and it includes 42 tutorials and demos.

22.12

Feb 28, 2022: Initial release of the research results for 34 papers

Our first release includes the algorithms, auxiliary functions, and data needed to recreate the results of over 150+ experiments that are discussed in 34 papers, thus allowing the reproduction of over 180 figures and tables. Additionally, we have included 32 tutorials and demos showing how the proposed algorithms can be used in your own applications.

22.02

Reproduce experiments

Apply algorithms

Complements Tensorlab

Read about our philosophy

Download code for individual papers

From multilinear SVD to multilinear UTV decomposition

Updating the multilinear UTV decomposition

Towards more realistic simulations of microstructural evolution in oxidic systems

Block row Kronecker-structured linear systems with a low-rank tensor solution

Canonical polyadic decomposition via the generalized Schur decomposition

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

Regression and classification with spline-based separable expansions

A recursive eigenspace computation for the canonical polyadic decomposition

Numerical methods for the computation of tensor decompositions

Bilinear factorizations subject to monomial equality constraints via tensor decompositions

Inexact generalized Gauss–Newton for scaling the canonical polyadic decomposition with non-least-squares cost functions

From computation to comparison of tensor decompositions

A quadratically convergent proximal algorithm for nonnegative tensor decomposition

A second-order method for fitting the canonical polyadic decomposition with non-least-squares cost

On uniqueness and computation of the decomposition of a tensor into multilinear rank-$(1, L_r, L_r)$ terms

Combining thermodynamics with tensor completion techniques to enable multicomponent microstructure prediction

Algebraic and optimization based algorithms for multivariate regression using symmetric tensor decomposition

Fiber sampling approach to canonical polyadic decomposition and application to tensor completion

Exploiting efficient representations in large-scale tensor decompositions

Numerical optimization-based algorithms for data fusion

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

Coupled canonical polyadic decompositions and multiple shift invariance in array processing

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

Face Recognition as a Kronecker product equation

Tensor-based large-scale blind system identification using segmentation

Tensor decompositions with several block-Hankel factors and application in blind system identification

Nonlinear least squares updating of the canonical polyadic decomposition

Blind multichannel deconvolution and convolutive extensions of canonical polyadic and block term decompositions

Nonnegative matrix factorization using nonnegative polynomial approximations

Irregular heartbeat classification using Kronecker product equations

Multidimensional harmonic retrieval via coupled canonical polyadic decomposition — Part II: Algorithm and multirate sampling

Canonical polyadic decomposition of third-order tensors: Relaxed uniqueness conditions and algebraic algorithm

A tensor-based method for large-scale blind source separation using segmentation

Multiple invariance ESPRIT for nonuniform linear arrays: A coupled canonical polyadic decomposition approach

Löwner-based blind signal separation of rational functions with applications

A randomized block sampling approach to canonical polyadic decomposition of large-scale tensors

New uniqueness conditions for the canonical polyadic decomposition of third-order tensors

Coupled canonical polyadic decompositions and (coupled) decompositions in multilinear rank-$(L_{r,n}, L_{r,n}, 1)$ terms — Part II: Algorithms

Breaking the curse of dimensionality using decompositions of incomplete tensors: Tensor-based scientiﬁc computing in big data analysis

Blind signal separation via tensor decomposition with Vandermonde factor: Canonical polyadic decomposition

Canonical polyadic decomposition with a columnwise orthonormal factor matrix

Blind identification of underdetermined mixtures by simultaneous matrix diagonalization

Decompositions of a higher-order tensor in block terms — Part III: Alternating least squares algorithms

Fourth-order cumulant-based blind identification of underdetermined mixtures

News

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