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)

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.

Conference

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

Fingerprint

Principal component analysis
Communication

Keywords

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

Cite this

Koutsogiannis, G., & Soraghan, J. J. (2002). Kernel principal component analysis (KPCA) for the de-noising of communication signals. 317-320. Paper presented at 11th European Signal Processing Conference EUSIPCO'2002, Toulouse, France.
Koutsogiannis, G. ; Soraghan, J.J. / Kernel principal component analysis (KPCA) for the de-noising of communication signals. Paper presented at 11th European Signal Processing Conference EUSIPCO'2002, Toulouse, France.
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Koutsogiannis, G & Soraghan, JJ 2002, 'Kernel principal component analysis (KPCA) for the de-noising of communication signals' Paper presented at 11th European Signal Processing Conference EUSIPCO'2002, Toulouse, France, 3/09/02 - 6/09/02, pp. 317-320.

Kernel principal component analysis (KPCA) for the de-noising of communication signals. / Koutsogiannis, G.; Soraghan, J.J.

2002. 317-320 Paper presented at 11th European Signal Processing Conference EUSIPCO'2002, Toulouse, France.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Kernel principal component analysis (KPCA) for the de-noising of communication signals

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AU - Soraghan, J.J.

PY - 2002

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N2 - 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.

AB - 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.

KW - de-noising

KW - non-linear signals

KW - principal component analysis

KW - kernel functions

UR - http://www.eurasip.org/Proceedings/Eusipco/2002/articles/paper049.html

M3 - Paper

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ER -

Koutsogiannis G, Soraghan JJ. Kernel principal component analysis (KPCA) for the de-noising of communication signals. 2002. Paper presented at 11th European Signal Processing Conference EUSIPCO'2002, Toulouse, France.