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 (http://dx.doi.org/10.1049/el:20020424)
Full text not available in this repository.Request a copyAbstract
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.
ORCID iDs
Koutsogiannis, G. and Soraghan, J.J. ORCID: https://orcid.org/0000-0003-4418-7391;-
-
Item type: Article ID code: 7082 Dates: DateEvent2002PublishedSubjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering Depositing user: Strathprints Administrator Date deposited: 31 Oct 2008 Last modified: 11 Nov 2024 08:38 URI: https://strathprints.strath.ac.uk/id/eprint/7082
CORE (COnnecting REpositories)