Sabine Van Huffel

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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)
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)
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)
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)
Multimodal approaches are of growing interest in the study of neural processes. To this end much attention has been paid to the integration of electroencephalographic (EEG) and functional magnetic resonance imaging (fMRI) data because of their complementary properties. However, the simultaneous acquisition of both types of data causes serious artifacts in(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 quadratically constrained Least Squares problem can be solved via a quadratic eigenvalue problem, an iterative(More)
The Total Least Squares (TLS) method is a generalization of the least squares (LS) method for solving overdetermined sets of linear equations Ax b. The TLS method minimizes jjEj?r]jj F where r = b?(A+E)x, so that (b?r) 2 Range(A+E), given A 2 C mn , with m n and b 2 C m1. The most common TLS algorithm is based on the singular value decomposition (SVD) of A(More)
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)
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)
This paper presents a new subspace-based technique for automatic detection of the number of exponentially damped sinusoids. It consists in studying the shift-invariance of the dominant subspace of the Hankel data matrix. No threshold setting and no penalization terms are necessary. This model-based method, easy to implement, can be plugged into most(More)