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

Koutsogiannis, G. and Soraghan, J.J. (2002) Kernel principal component analysis (KPCA) for the de-noising of communication signals. In: 11th European Signal Processing Conference EUSIPCO'2002, 2002-09-03 - 2002-09-06.

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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.

ORCID iDs

Koutsogiannis, G. and Soraghan, J.J. ORCID logoORCID: https://orcid.org/0000-0003-4418-7391;
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