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Extraction of intrinsic dimension using CCA-Application to blind sources separation.

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Lendasse, Amaury [UCL] Verleysen, Michel [UCL] Donckers, Nicolas [UCL] Wertz, Vincent [UCL]

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.

Document type Communication à un colloque (Conference Paper) – Présentation orale avec comité de sélection
Access type Accès restreint
Publication date 1999
Language Anglais
Conference "European Symposium on Artificial Neural Networks (ESANN'99)", Bruges (Belgium) (du 21/04/1999 au 23/04/1999)
Peer reviewed yes
Host document "Proceedings of the European Symposium on Artificial Neural Networks (ESANN'99)"- p. 339-344
Publisher D-Facto public.
Affiliations UCL - FSA/INMA - Département d'ingénierie mathématique
UCL - FSA/ELEC - Département d'électricité
Links
Bibliographic reference Lendasse, Amaury ; Verleysen, Michel ; Donckers, Nicolas ; Wertz, Vincent. Extraction of intrinsic dimension using CCA-Application to blind sources separation..European Symposium on Artificial Neural Networks (ESANN'99) (Bruges (Belgium), du 21/04/1999 au 23/04/1999). In: Proceedings of the European Symposium on Artificial Neural Networks (ESANN'99), D-Facto public.1999, p. 339-344
Permanent URL http://hdl.handle.net/2078.1/111360