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- JГ¶rg Liesen, Zdenek Strakos, Shun Wang, Feliks Ruvimovich, Sabine Van Huffel, Joos Vandewalle
- 2009

The mathematical theory of Krylov subspace methods with a focus on solving systems of linear algebraic equations is given a detailed treatment in this principles-based book. Starting from the idea of projections, Krylov subspace methods are characterised by their orthogonality and minimisation properties. Projections onto highly nonlinear Krylov subspaces… (More)

- Ivan Markovsky, Sabine Van Huffel
- Signal Processing
- 2007

We review the development and extensions of the classical total least squares method and describe algorithms for its generalization to weighted and structured approximation problems. In the generic case, the classical total least squares problem has a unique solution, which is given in analytic form in terms of the singular value decomposition of the data… (More)

- Sabine Van Huffel, Chi-Lun Cheng, Nicola Mastronardi, Chris Paige, Alexander Kukush
- Computational Statistics & Data Analysis
- 2007

The main purpose of this special issue is to present an overview of the progress of a modeling technique which is known as total least squares (TLS) in computational mathematics and engineering, and as errors-in-variables (EIV) modeling or orthogonal regression in the statistical community. The TLS method is one of several linear parameter estimation… (More)

- Giansalvo Cirrincione, Maurizio Cirrincione, Jeanny Hérault, Sabine Van Huffel
- IEEE Trans. Neural Networks
- 2002

The minor component analysis (MCA) deals with the recovery of the eigenvector associated to the smallest eigenvalue of the autocorrelation matrix of the input data and is a very important tool for signal processing and data analysis. It is almost exclusively solved by linear neurons. This paper presents a linear neuron endowed with a novel learning law,… (More)

- Wim De Clercq, Anneleen Vergult, Bart Vanrumste, Wim Van Paesschen, Sabine Van Huffel
- IEEE Trans. Biomed. Engineering
- 2006

The electroencephalogram (EEG) is often contaminated by muscle artifacts. In this paper, a new method for muscle artifact removal in EEG is presented, based on canonical correlation analysis (CCA) as a blind source separation (BSS) technique. This method is demonstrated on a synthetic data set. The method outperformed a low-pass filter with different cutoff… (More)

This paper presents a new computational approach for solving the Regularized Total Least Squares problem. The problem is formulated by adding a quadratic constraint to the Total Least Square minimization problem. Starting from the fact that a quadrat-ically constrained Least Squares problem can be solved via a quadratic eigenvalue problem, an iterative… (More)

- Vanya Van Belle, Kristiaan Pelckmans, Sabine Van Huffel, Johan A. K. Suykens
- Artificial Intelligence in Medicine
- 2011

OBJECTIVE
To compare and evaluate ranking, regression and combined machine learning approaches for the analysis of survival data.
METHODS
The literature describes two approaches based on support vector machines to deal with censored observations. In the first approach the key idea is to rephrase the task as a ranking problem via the concordance index, a… (More)

- L Vanhamme, T Sundin, P V Hecke, S V Huffel
- NMR in biomedicine
- 2001

In this article an overview of time-domain quantitation methods is given. Advantages of processing the data in the measurement domain are discussed. The basic underlying principles of the methods are outlined and from them the situations under which these algorithms perform well are derived. Also an overview of methods to preprocess the data is given. In… (More)

- Bogdan Mijovic, Maarten De Vos, Ivan Gligorijevic, Joachim Taelman, Sabine Van Huffel
- IEEE Trans. Biomed. Engineering
- 2010

In biomedical signal processing, it is often the case that many sources are mixed into the measured signal. The goal is usually to analyze one or several of them separately. In the case of multichannel measurements, several blind source separation techniques are available for decomposing the signal into its components [e.g., independent component analysis… (More)

- Lukas Lukas, Andy Devos, +8 authors Sabine Van Huffel
- Artificial Intelligence in Medicine
- 2004

There has been a growing research interest in brain tumor classification based on proton magnetic resonance spectroscopy (1H MRS) signals. Four research centers within the EU funded INTERPRET project have acquired a significant number of long echo 1H MRS signals for brain tumor classification. In this paper, we present an objective comparison of several… (More)