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)

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.

ORCID iDs

Soraghan, J.J. ORCID: https://orcid.org/0000-0003-4418-7391

• Share and Export
  

  ReferAtomJSONOpenURL ContextObject in SpanMPEG-21 DIDLRDF+XMLRDF+N-TriplesRefWorksHTML CitationEndNoteMultiline CSVRIS (EndNote Online, Zotero, Ref manager, etc)Data Cite XMLDublin CoreEP3 XMLASCII CitationRDF+N3METSSimple MetadataRIOXX2 XMLBibTeXOpenURL ContextObjectMODS
• Item metadata

  Item type:	Article
  ID code:	7082
  Dates:	Date Event 2002 Published
  Subjects:	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