Selection of number of principal components for de-noising signals

G. Koutsogiannis; J.J. Soraghan

doi:10.1049/el:20020424

Selection of number of principal components for de-noising signals

G. Koutsogiannis, J.J. Soraghan

Electronic And Electrical Engineering

Research output: Contribution to journal › Article › peer-review

10 Citations (Scopus)

Abstract

Principal component analysis (PCA) is a transformation technique used to reduce the dimensionality of a dataset. Using delay embedding, it is possible to know a priori how many principal components to choose to obtain the optimum reconstruction. A novel nonlinear PCA-based scheme employing delay embedding is presented for the de-noising of communication signals.

Original language	English
Pages (from-to)	664-666
Number of pages	2
Journal	Electronics Letters
Volume	38
Issue number	13
DOIs	https://doi.org/10.1049/el:20020424
Publication status	Published - 2002

Keywords

minimum shift keying
principal component analysis
quadrature phase shift keying
signal reconstruction

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10.1049/el:20020424

http://dx.doi.org/10.1049/el:20020424

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AB - Principal component analysis (PCA) is a transformation technique used to reduce the dimensionality of a dataset. Using delay embedding, it is possible to know a priori how many principal components to choose to obtain the optimum reconstruction. A novel nonlinear PCA-based scheme employing delay embedding is presented for the de-noising of communication signals.

KW - minimum shift keying

KW - principal component analysis

KW - quadrature phase shift keying

KW - signal reconstruction

UR - http://dx.doi.org/10.1049/el:20020424

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