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

G. Koutsogiannis, J.J. Soraghan

Research output: Contribution to conferencePaper

1 Citation (Scopus)

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
Original languageEnglish
PagesII-1677-II1680
Number of pages4
DOIs
Publication statusPublished - May 2002
Event2002 IEEE International Conference on Acoustics, Speech, and Signal Processing - Renaissance Orlando Resort, Orlando, United States
Duration: 13 May 200217 May 2002

Conference

Conference2002 IEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated titleICASSP 2002
CountryUnited States
CityOrlando
Period13/05/0217/05/02

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

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