Picture of a sphere with binary code

Making Strathclyde research discoverable to the world...

The Strathprints institutional repository is a digital archive of University of Strathclyde research outputs. It exposes Strathclyde's world leading Open Access research to many of the world's leading resource discovery tools, and from there onto the screens of researchers around the world.

Explore Strathclyde Open Access research content

Selection of number of principal components for de-noising signals

Koutsogiannis, G. and Soraghan, J.J. (2002) Selection of number of principal components for de-noising signals. Electronics Letters, 38 (13). pp. 664-666. ISSN 0013-5194

Full text not available in this repository. (Request a copy from the Strathclyde author)

Abstract

Principal component analysis (PCA) is a transformation technique used to reduce the dimensionality of a dataset. Using delay embedding, it is possible to know a priori how many principal components to choose to obtain the optimum reconstruction. A novel nonlinear PCA-based scheme employing delay embedding is presented for the de-noising of communication signals.