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

G. Koutsogiannis; J.J. Soraghan

doi:10.1109/ICASSP.2002.5744942

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

G. Koutsogiannis, J.J. Soraghan

Electronic And Electrical Engineering

Research output: Contribution to conference › Paper

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 language	English
Pages	II-1677-II1680
Number of pages	4
DOIs	https://doi.org/10.1109/ICASSP.2002.5744942
Publication status	Published - May 2002
Event	2002 IEEE International Conference on Acoustics, Speech, and Signal Processing - Renaissance Orlando Resort, Orlando, United States Duration: 13 May 2002 → 17 May 2002

Conference

Conference	2002 IEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated title	ICASSP 2002
Country/Territory	United States
City	Orlando
Period	13/05/02 → 17/05/02

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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10.1109/ICASSP.2002.5744942

Cite this

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

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",

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year = "2002",

month = may,

doi = "10.1109/ICASSP.2002.5744942",

language = "English",

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AU - Koutsogiannis, G.

AU - Soraghan, J.J.

PY - 2002/5

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N2 - 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

AB - 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

KW - classification

KW - de-noising

KW - communication signals

KW - kernel principal component analysis

KW - kpca

KW - artificial neural networks

KW - transforms

KW - support vector machines

KW - principal component analysis

KW - noise measurement

KW - kernel

KW - feature extraction

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DO - 10.1109/ICASSP.2002.5744942

M3 - Paper

SP - II-1677-II1680

T2 - 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing

Y2 - 13 May 2002 through 17 May 2002

ER -

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

Abstract

Conference

Keywords

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