A parameter-free Cauchy-Schwartz information measure for independent component analysis

Lei Sun; Badong Chen; Kar Ann Ton; Zhiping Lin

doi:10.1109/ICASSP.2016.7472132

A parameter-free Cauchy-Schwartz information measure for independent component analysis

Lei Sun, Badong Chen, Kar Ann Ton, Zhiping Lin

集成电路与电子学院

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

摘要

Independent component analysis (ICA) by an information measure has seen wide applications in engineering. Different from traditional probability density function based information measures, a probability survival distribution based Cauchy-Schwartz information measure for multiple variables is proposed in this paper. Empirical estimation of survival distribution is parameter-free which is inherited by the estimation of the new information measure. This measure is proved to be a valid statistical independence measure and is adopted as an objective function to develop an ICA algorithm which is validated by an experiment. This work shows promising potential regarding the use of survival distribution based information measure for ICA.

源语言	英语
主期刊名	2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
出版商	Institute of Electrical and Electronics Engineers Inc.
页	2524-2528
页数	5
ISBN（电子版）	9781479999880
DOI	https://doi.org/10.1109/ICASSP.2016.7472132
出版状态	已出版 - 18 5月 2016
活动	41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, 中国期限: 20 3月 2016 → 25 3月 2016

出版系列

姓名	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
卷	2016-May
ISSN（印刷版）	1520-6149

会议

会议	41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
国家/地区	中国
市	Shanghai
时期	20/03/16 → 25/03/16

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10.1109/ICASSP.2016.7472132

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Sun, L., Chen, B., Ton, K. A., & Lin, Z. (2016). A parameter-free Cauchy-Schwartz information measure for independent component analysis. 在 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings (页码 2524-2528). 文章 7472132 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; 卷 2016-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2016.7472132

Sun, Lei ; Chen, Badong ; Ton, Kar Ann 等. / A parameter-free Cauchy-Schwartz information measure for independent component analysis. 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. 页码 2524-2528 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

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title = "A parameter-free Cauchy-Schwartz information measure for independent component analysis",

abstract = "Independent component analysis (ICA) by an information measure has seen wide applications in engineering. Different from traditional probability density function based information measures, a probability survival distribution based Cauchy-Schwartz information measure for multiple variables is proposed in this paper. Empirical estimation of survival distribution is parameter-free which is inherited by the estimation of the new information measure. This measure is proved to be a valid statistical independence measure and is adopted as an objective function to develop an ICA algorithm which is validated by an experiment. This work shows promising potential regarding the use of survival distribution based information measure for ICA.",

keywords = "Blind Signal Separation, Independent Component Analysis, Information Measure, Probability Survival Distribution",

author = "Lei Sun and Badong Chen and Ton, {Kar Ann} and Zhiping Lin",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 ; Conference date: 20-03-2016 Through 25-03-2016",

year = "2016",

month = may,

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doi = "10.1109/ICASSP.2016.7472132",

language = "English",

series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings",

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Sun, L, Chen, B, Ton, KA & Lin, Z 2016, A parameter-free Cauchy-Schwartz information measure for independent component analysis. 在 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings., 7472132, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 卷 2016-May, Institute of Electrical and Electronics Engineers Inc., 页码 2524-2528, 41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016, Shanghai, 中国, 20/03/16. https://doi.org/10.1109/ICASSP.2016.7472132

A parameter-free Cauchy-Schwartz information measure for independent component analysis. / Sun, Lei; Chen, Badong; Ton, Kar Ann 等.
2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2016. 页码 2524-2528 7472132 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; 卷 2016-May).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - A parameter-free Cauchy-Schwartz information measure for independent component analysis

AU - Sun, Lei

AU - Chen, Badong

AU - Ton, Kar Ann

AU - Lin, Zhiping

PY - 2016/5/18

Y1 - 2016/5/18

N2 - Independent component analysis (ICA) by an information measure has seen wide applications in engineering. Different from traditional probability density function based information measures, a probability survival distribution based Cauchy-Schwartz information measure for multiple variables is proposed in this paper. Empirical estimation of survival distribution is parameter-free which is inherited by the estimation of the new information measure. This measure is proved to be a valid statistical independence measure and is adopted as an objective function to develop an ICA algorithm which is validated by an experiment. This work shows promising potential regarding the use of survival distribution based information measure for ICA.

AB - Independent component analysis (ICA) by an information measure has seen wide applications in engineering. Different from traditional probability density function based information measures, a probability survival distribution based Cauchy-Schwartz information measure for multiple variables is proposed in this paper. Empirical estimation of survival distribution is parameter-free which is inherited by the estimation of the new information measure. This measure is proved to be a valid statistical independence measure and is adopted as an objective function to develop an ICA algorithm which is validated by an experiment. This work shows promising potential regarding the use of survival distribution based information measure for ICA.

KW - Blind Signal Separation

KW - Independent Component Analysis

KW - Information Measure

KW - Probability Survival Distribution

UR - http://www.scopus.com/inward/record.url?scp=84973333016&partnerID=8YFLogxK

U2 - 10.1109/ICASSP.2016.7472132

DO - 10.1109/ICASSP.2016.7472132

M3 - Conference contribution

AN - SCOPUS:84973333016

T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

SP - 2524

EP - 2528

BT - 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016

Y2 - 20 March 2016 through 25 March 2016

ER -

Sun L, Chen B, Ton KA, Lin Z. A parameter-free Cauchy-Schwartz information measure for independent component analysis. 在 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2016. 页码 2524-2528. 7472132. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP.2016.7472132

A parameter-free Cauchy-Schwartz information measure for independent component analysis

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