Independent component analysis (ICA) is a data analysis tool that can be seen as a
refinement of principal component analysis or factor analysis. ICA recovers the structures in …

The viewpoint taken in much of this text is that PCA is mainly a descriptive tool with no need
for rigorous distributional or model assumptions. This implies that it can be used on a wide …

A general method for exploring multivariate data by comparing different estimates of
multivariate scatter is presented. The method is based on the eigenvalue–eigenvector …

The present paper examines proportional goodness of fit to variables recorded on
individuals, the variances and covariances of the variables, and the form and distances …

In independent component analysis it is assumed that the components of the observed
random vector are linear combinations of latent independent random variables, and the aim …

Many linear dimension reduction methods proposed in the literature can be formulated
using an appropriate pair of scatter matrices. The eigen-decomposition of one scatter matrix …

Invariant coordinate selection (ICS) has recently been introduced as a method for exploring
multivariate data. It includes as a special case a method for recovering the unmixing matrix …

Le biplot est un outil graphique pour visualiser des données arrangées en forme de matrice
(Gabriel, 1971, 1982; Gower et Hand, 1996). Les graphiques ont des usages divers en …

In high reliability standards fields such as automotive, avionics or aerospace, the detection
of anomalies is crucial. An efficient methodology for automatically detecting multivariate …

In this paper we study the properties of a kurtosis matrix and propose its eigenvectors as
interesting directions to reveal the possible cluster structure of a data set. Under a mixture of …