Selection of number of principal components for de-noising signals

G. Koutsogiannis, J.J. Soraghan

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)664-666
Number of pages2
JournalElectronics Letters
Issue number13
Publication statusPublished - 2002


  • minimum shift keying
  • principal component analysis
  • quadrature phase shift keying
  • signal reconstruction


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