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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)

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)

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)

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)

1 This report is available by anonymous ftp from ftp.esat.kuleuven.ac.be in the directory pub/SISTA/lemmerli/reports/int9889.ps.Z. Abstract In this paper we develop a fast algorithm for the basic deconvolution problem. First we show that the kernel problem to be solved in the basic de-convolution problem is a so-called structured Total Least Squares… (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)

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)

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)

Recent studies show that principal component analysis (PCA) of heartbeats is a well-performing method to derive a respiratory signal from ECGs. In this study, an improved ECG-derived respiration (EDR) algorithm based on kernel PCA (kPCA) is presented. KPCA can be seen as a generalization of PCA where nonlinearities in the data are taken into account by… (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)