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Classification and de-noising of communication signals using kernel principal component analysis (KPCA)

Koutsogiannis, G. and Soraghan, J.J. (2002) Classification and de-noising of communication signals using kernel principal component analysis (KPCA). In: 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2002-05-13 - 2002-05-17, Orlando.

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Abstract

This paper is concerned with the classification and de-noising problem for non-linear signals. 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 the feature space, the signal can be classified correctly in its input space. Additionally, it is shown how this classification process' can be used to de-noise DQPSK communication signals

Item type: Conference or Workshop Item (Paper)
ID code: 39480
Keywords: classification, de-noising, communication signals, kernel principal component analysis , kpca, artificial neural networks , transforms , support vector machines, principal component analysis, noise measurement, kernel , feature extraction , Electrical engineering. Electronics Nuclear engineering
Subjects: Technology > Electrical engineering. Electronics Nuclear engineering
Department: Faculty of Engineering > Electronic and Electrical Engineering
Related URLs:
    Depositing user: Pure Administrator
    Date Deposited: 02 May 2012 15:17
    Last modified: 06 Sep 2014 15:10
    URI: http://strathprints.strath.ac.uk/id/eprint/39480

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