An ICA algorithm with adaptive-learned polynomial nonlinearity for signal separation

Yiu ming Cheung; Lei Xu

doi:10.1109/IJCNN.1999.831082

An ICA algorithm with adaptive-learned polynomial nonlinearity for signal separation

Yiu ming Cheung
, Lei Xu

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

Abstract

This paper presents a novel approach called Adaptive Polynomial Power Learning Estimation (APPLE) based ICA Algorithm for independent component analysis (ICA) problem. In this algorithm, the form of separation nonlinearity is fixed at polynomial function, but the exponent is adaptive adjusted in implementation. Experiments have demonstrated that this algorithm can successfully separate the combinations of sub-Gaussian and super-Gaussian signals.

Original language	English
Title of host publication	IJCNN'99. International Joint Conference on Neural Networks. Proceedings
Publisher	IEEE
Pages	955-960
Number of pages	6
ISBN (Print)	0780355296, 078035530X, 0780355318
DOIs	https://doi.org/10.1109/IJCNN.1999.831082
Publication status	Published - 10 Jul 1999
Event	International Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA Duration: 10 Jul 1999 → 16 Jul 1999

Publication series

Name	International Joint Conference on Neural Networks - Proceedings
ISSN (Print)	1098-7576

Conference

Conference	International Joint Conference on Neural Networks (IJCNN'99)
City	Washington, DC, USA
Period	10/07/99 → 16/07/99

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10.1109/IJCNN.1999.831082

Cite this

@inproceedings{1f136cd6986c4f90a712fee55825d621,

title = "An ICA algorithm with adaptive-learned polynomial nonlinearity for signal separation",

abstract = "This paper presents a novel approach called Adaptive Polynomial Power Learning Estimation (APPLE) based ICA Algorithm for independent component analysis (ICA) problem. In this algorithm, the form of separation nonlinearity is fixed at polynomial function, but the exponent is adaptive adjusted in implementation. Experiments have demonstrated that this algorithm can successfully separate the combinations of sub-Gaussian and super-Gaussian signals.",

author = "Cheung, \{Yiu ming\} and Lei Xu",

note = "Publisher Copyright: {\textcopyright} 1999 IEEE ; International Joint Conference on Neural Networks (IJCNN'99) ; Conference date: 10-07-1999 Through 16-07-1999",

year = "1999",

month = jul,

day = "10",

doi = "10.1109/IJCNN.1999.831082",

language = "English",

isbn = "0780355296",

series = "International Joint Conference on Neural Networks - Proceedings",

publisher = "IEEE",

pages = "955--960",

booktitle = "IJCNN'99. International Joint Conference on Neural Networks. Proceedings",

address = "United States",

}

Cheung, YM & Xu, L 1999, An ICA algorithm with adaptive-learned polynomial nonlinearity for signal separation. in IJCNN'99. International Joint Conference on Neural Networks. Proceedings. International Joint Conference on Neural Networks - Proceedings, IEEE, pp. 955-960, International Joint Conference on Neural Networks (IJCNN'99), Washington, DC, USA, 10/07/99. https://doi.org/10.1109/IJCNN.1999.831082

An ICA algorithm with adaptive-learned polynomial nonlinearity for signal separation. / Cheung, Yiu ming; Xu, Lei.
IJCNN'99. International Joint Conference on Neural Networks. Proceedings. IEEE, 1999. p. 955-960 (International Joint Conference on Neural Networks - Proceedings).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - An ICA algorithm with adaptive-learned polynomial nonlinearity for signal separation

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AU - Xu, Lei

PY - 1999/7/10

Y1 - 1999/7/10

N2 - This paper presents a novel approach called Adaptive Polynomial Power Learning Estimation (APPLE) based ICA Algorithm for independent component analysis (ICA) problem. In this algorithm, the form of separation nonlinearity is fixed at polynomial function, but the exponent is adaptive adjusted in implementation. Experiments have demonstrated that this algorithm can successfully separate the combinations of sub-Gaussian and super-Gaussian signals.

AB - This paper presents a novel approach called Adaptive Polynomial Power Learning Estimation (APPLE) based ICA Algorithm for independent component analysis (ICA) problem. In this algorithm, the form of separation nonlinearity is fixed at polynomial function, but the exponent is adaptive adjusted in implementation. Experiments have demonstrated that this algorithm can successfully separate the combinations of sub-Gaussian and super-Gaussian signals.

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U2 - 10.1109/IJCNN.1999.831082

DO - 10.1109/IJCNN.1999.831082

M3 - Conference proceeding

AN - SCOPUS:0033307275

SN - 0780355296

SN - 078035530X

SN - 0780355318

T3 - International Joint Conference on Neural Networks - Proceedings

SP - 955

EP - 960

BT - IJCNN'99. International Joint Conference on Neural Networks. Proceedings

PB - IEEE

T2 - International Joint Conference on Neural Networks (IJCNN'99)

Y2 - 10 July 1999 through 16 July 1999

ER -

An ICA algorithm with adaptive-learned polynomial nonlinearity for signal separation

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