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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, Toulouse.

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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.

    Item type: Conference or Workshop Item (Paper)
    ID code: 39300
    Keywords: de-noising, non-linear signals, principal component analysis , kernel functions, Electrical engineering. Electronics Nuclear engineering
    Subjects: Technology > Electrical engineering. Electronics Nuclear engineering
    Department: Faculty of Engineering > Electronic and Electrical Engineering
    Unknown Department
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    Depositing user: Pure Administrator
    Date Deposited: 23 Apr 2012 13:05
    Last modified: 20 Jul 2013 21:34
    URI: http://strathprints.strath.ac.uk/id/eprint/39300

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