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, Renaissance Orlando Resort. (https://doi.org/10.1109/ICASSP.2002.5744942)
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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
ORCID iDs
Koutsogiannis, G. and Soraghan, J.J. ORCID: https://orcid.org/0000-0003-4418-7391;-
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Item type: Conference or Workshop Item(Paper) ID code: 39480 Dates: DateEventMay 2002PublishedSubjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering Depositing user: Pure Administrator Date deposited: 02 May 2012 14:17 Last modified: 11 Nov 2024 16:16 URI: https://strathprints.strath.ac.uk/id/eprint/39480