Kernel principal component analysis (KPCA) for the de-noising of communication signals

G. Koutsogiannis, J.J. Soraghan

Research output: Contribution to conferencePaper

2 Citations (Scopus)
34 Downloads (Pure)

Abstract

This paper is concerned with the problem of de-noising for non-linear signals. Principal Component Analysis (PCA) cannot be applied to non-linear signals however it is known that using kernel functions, a non-linear signal can be transformed into a linear signal in a higher dimensional space. In that feature space, a linear algorithm can be applied to a non-linear problem. It is proposed that using the principal components extracted from this feature space, the signal can be de-noised in its input space.
Original languageEnglish
Pages317-320
Publication statusPublished - 2002
Event11th European Signal Processing Conference EUSIPCO'2002 - Toulouse, France
Duration: 3 Sep 20026 Sep 2002

Conference

Conference11th European Signal Processing Conference EUSIPCO'2002
CountryFrance
CityToulouse
Period3/09/026/09/02

Keywords

  • de-noising
  • non-linear signals
  • principal component analysis
  • kernel functions

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