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- Laurent Sorber, Marc Van Barel, Lieven De Lathauwer
- SIAM Journal on Optimization
- 2013

The canonical polyadic and rank-(Lr , Lr , 1) block term decomposition (CPD and BTD, respectively) are two closely related tensor decompositions. The CPD and, recently, BTD are important tools in psychometrics, chemometrics, neuroscience, and signal processing. We present a decomposition that generalizes these two and develop algorithms for its computation.… (More)

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

We present structured data fusion (SDF) as a framework for the rapid prototyping of knowledge discovery in one or more possibly incomplete data sets. In SDF, each data set-stored as a dense, sparse, or incomplete tensor-is factorized with a matrix or tensor decomposition. Factorizations can be coupled, or fused, with each other by indicating which factors… (More)

- Nico Vervliet, Otto Debals, Laurent Sorber, Lieven De Lathauwer
- IEEE Signal Processing Magazine
- 2014

Higher-order tensors and their decompositions are abundantly present in domains such as signal processing (e.g., higher-order statistics [1] and sensor array processing [2]), scientific computing (e.g., discretized multivariate functions [3]?[6]), and quantum information theory (e.g., representation of quantum many-body states [7]). In many applications,… (More)

- Laurent Sorber, Marc Van Barel, Lieven De Lathauwer
- SIAM J. Numerical Analysis
- 2014

- Laurent Sorber, Marc Van Barel, Lieven De Lathauwer
- SIAM Journal on Optimization
- 2012

Nonlinear optimization problems in complex variables are frequently encountered in applied mathematics and engineering applications such as control theory, signal processing and electrical engineering. Optimization of these problems often requires a firstor second-order approximation of the objective function to generate a new step or descent direction.… (More)

- Borbála Hunyadi, Daan Camps, +4 authors Lieven De Lathauwer
- EURASIP J. Adv. Sig. Proc.
- 2014

Recordings of neural activity, such as EEG, are an inherent mixture of different ongoing brain processes as well as artefacts and are typically characterised by low signal-to-noise ratio. Moreover, EEG datasets are often inherently multidimensional, comprising information in time, along different channels, subjects, trials, etc. Additional information may… (More)

- Laurent Sorber, Ignat Domanov, Marc Van Barel, Lieven De Lathauwer
- Comp. Opt. and Appl.
- 2016

Efficient and effective algorithms are designed to compute the coordinates of nearly optimal points for multivariate polynomial interpolation on a general geometry. “Nearly optimal” refers to the property that the set of points has a Lebesgue constant near to the minimal Lebesgue constant with respect to multivariate polynomial interpolation on a finite… (More)

In this paper, we conjecture a formula for the value of the Pythagoras number for real multivariate sum of squares polynomials as a function of the (total or coordinate) degree and the number of variables. The conjecture is based on the comparison between the number of parameters and the number of conditions for a corresponding low-rank representation. This… (More)

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