Extraction of intrinsic dimension using CCA-Application to blind sources separation.
Amaury Lendasse, Michel Verleysen, Nicolas Donckers, Vincent Wertz · Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)) · 1999
A general-purpose useful parameter in data analysis is the intrinsic dimension of a data set, corresponding to the minimum number of variables necessary to describe the data without significant loss of information. The knowledge of this dimension also facilitates most non-linear projection methods. We will show that the intrinsic dimension of a data set can be efficiently estimated using Curvilinear Component Analysis; we will also show that the method can be applied to the Blind Source Separation problem to estimate the number of sources in a mixing.