Koutsogiannis, G. and Soraghan, J.J. (2002) Classification and de-noising of communication signals using kernel principal component analysis (KPCA). In: IEEE International Conference on Acoustics, Speech, and Signal Processing, 2002-05-13 - 2002-05-17, Orlando.
Full text not available in this repository. (Request a copy from the Strathclyde author)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: | 04 Oct 2012 17:13 |
| URI: | http://strathprints.strath.ac.uk/id/eprint/39480 |
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