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

Lendasse, Amaury;Verleysen, Michel;Donckers, Nicolas;Wertz, Vincent
(1999) European Symposium on Artificial Neural Networks (ESANN′99) — Location: Bruges (Belgium) (21.April.1999)

Files

47-ExtractionofintrinsicdimensionusingCCA-Applicationtoblindsourcesseparation.pdf
  • Restricted Access
  • Adobe PDF
  • 100.6 KB
Request a copy

Details

Authors
  • Lendasse, AmauryUCLouvain
    Author
  • Verleysen, MichelUCLouvain
    Author
  • Donckers, NicolasUCLouvain
    Author
  • Wertz, VincentUCLouvain
    Author
Abstract
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.
Affiliations

Citations

  • APA
  • Chicago
  • FWB

Lendasse, A., Verleysen, M., Donckers, N., & Wertz, V. (1999). Extraction of intrinsic dimension using CCA-Application to blind sources separation. Proceedings of the European Symposium on Artificial Neural Networks (ESANN′99), p. 339-344. https://hdl.handle.net/2078.5/253748