A Learning Framework for Blind Source Separation Using Generalized Eigenvalues

Hailin Liu; Yiu Ming Cheung

doi:10.1007/11427445_77

A Learning Framework for Blind Source Separation Using Generalized Eigenvalues

Hailin Liu, Yiu Ming Cheung

Department of Computer Science

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

4 Citations (Scopus)

Abstract

This paper presents a learning framework for blind source separation (BSS), in which the BSS is formulated as generalized Eigenvalue (GE) problem. Compared to the typical information-theoretical approaches, this new one has at least two merits: (1) the unknown unmixing matrix directly works out from the GE equation without time-consuming iterative learning; (2) The correctness of the solution is guaranteed. We give out a general learning procedure under this framework. The computer simulation shows validity of our method.

Original language	English
Title of host publication	Advances in Neural Networks - ISNN 2005
Subtitle of host publication	Second International Symposium on Neural Networks, Chongqing, China, May 30 - June 1, 2005, Proceedings, Part II
Editors	Jun Wang, Xiao-Feng Liao, Zhang Yi
Place of Publication	Berlin, Heidelberg
Publisher	Springer
Pages	472-477
Number of pages	6
Edition	1st
ISBN (Electronic)	9783540320678
ISBN (Print)	9783540259138
DOIs	https://doi.org/10.1007/11427445_77
Publication status	Published - 4 May 2005
Event	Second International Symposium on Neural Networks: Advances in Neural Networks - ISNN 2005 - Chongqing, China Duration: 30 May 2005 → 1 Jun 2005 https://link.springer.com/book/10.1007/b136476

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	3497
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Name	Theoretical Computer Science and General Issues

Name	ISNN: International Symposium on Neural Networks

Conference

Conference	Second International Symposium on Neural Networks: Advances in Neural Networks - ISNN 2005
Country/Territory	China
City	Chongqing
Period	30/05/05 → 1/06/05
Internet address	https://link.springer.com/book/10.1007/b136476

Scopus Subject Areas

Theoretical Computer Science
Computer Science(all)

User-Defined Keywords

Independent Component Analysis
Blind Source Separation
Learn Framework
Generalize Eigenvalue Problem
Contrast Function

Access to Document

10.1007/11427445_77

Cite this

Liu, H., & Cheung, Y. M. (2005). A Learning Framework for Blind Source Separation Using Generalized Eigenvalues. In J. Wang, X.-F. Liao, & Z. Yi (Eds.), Advances in Neural Networks - ISNN 2005: Second International Symposium on Neural Networks, Chongqing, China, May 30 - June 1, 2005, Proceedings, Part II (1st ed., pp. 472-477). (Lecture Notes in Computer Science; Vol. 3497), (Theoretical Computer Science and General Issues), (ISNN: International Symposium on Neural Networks). Springer. https://doi.org/10.1007/11427445_77

Liu, Hailin ; Cheung, Yiu Ming. / A Learning Framework for Blind Source Separation Using Generalized Eigenvalues. Advances in Neural Networks - ISNN 2005: Second International Symposium on Neural Networks, Chongqing, China, May 30 - June 1, 2005, Proceedings, Part II. editor / Jun Wang ; Xiao-Feng Liao ; Zhang Yi. 1st. ed. Berlin, Heidelberg : Springer, 2005. pp. 472-477 (Lecture Notes in Computer Science). (Theoretical Computer Science and General Issues). (ISNN: International Symposium on Neural Networks).

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title = "A Learning Framework for Blind Source Separation Using Generalized Eigenvalues",

abstract = "This paper presents a learning framework for blind source separation (BSS), in which the BSS is formulated as generalized Eigenvalue (GE) problem. Compared to the typical information-theoretical approaches, this new one has at least two merits: (1) the unknown unmixing matrix directly works out from the GE equation without time-consuming iterative learning; (2) The correctness of the solution is guaranteed. We give out a general learning procedure under this framework. The computer simulation shows validity of our method.",

keywords = "Independent Component Analysis, Blind Source Separation, Learn Framework, Generalize Eigenvalue Problem, Contrast Function",

author = "Hailin Liu and Cheung, {Yiu Ming}",

note = "Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; Second International Symposium on Neural Networks: Advances in Neural Networks - ISNN 2005 ; Conference date: 30-05-2005 Through 01-06-2005",

year = "2005",

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editor = "Jun Wang and Xiao-Feng Liao and Zhang Yi",

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Liu, H & Cheung, YM 2005, A Learning Framework for Blind Source Separation Using Generalized Eigenvalues. in J Wang, X-F Liao & Z Yi (eds), Advances in Neural Networks - ISNN 2005: Second International Symposium on Neural Networks, Chongqing, China, May 30 - June 1, 2005, Proceedings, Part II. 1st edn, Lecture Notes in Computer Science, vol. 3497, Theoretical Computer Science and General Issues, ISNN: International Symposium on Neural Networks, Springer, Berlin, Heidelberg, pp. 472-477, Second International Symposium on Neural Networks: Advances in Neural Networks - ISNN 2005, Chongqing, China, 30/05/05. https://doi.org/10.1007/11427445_77

A Learning Framework for Blind Source Separation Using Generalized Eigenvalues. / Liu, Hailin; Cheung, Yiu Ming.
Advances in Neural Networks - ISNN 2005: Second International Symposium on Neural Networks, Chongqing, China, May 30 - June 1, 2005, Proceedings, Part II. ed. / Jun Wang; Xiao-Feng Liao; Zhang Yi. 1st. ed. Berlin, Heidelberg: Springer, 2005. p. 472-477 (Lecture Notes in Computer Science; Vol. 3497), (Theoretical Computer Science and General Issues), (ISNN: International Symposium on Neural Networks).

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

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N2 - This paper presents a learning framework for blind source separation (BSS), in which the BSS is formulated as generalized Eigenvalue (GE) problem. Compared to the typical information-theoretical approaches, this new one has at least two merits: (1) the unknown unmixing matrix directly works out from the GE equation without time-consuming iterative learning; (2) The correctness of the solution is guaranteed. We give out a general learning procedure under this framework. The computer simulation shows validity of our method.

AB - This paper presents a learning framework for blind source separation (BSS), in which the BSS is formulated as generalized Eigenvalue (GE) problem. Compared to the typical information-theoretical approaches, this new one has at least two merits: (1) the unknown unmixing matrix directly works out from the GE equation without time-consuming iterative learning; (2) The correctness of the solution is guaranteed. We give out a general learning procedure under this framework. The computer simulation shows validity of our method.

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KW - Blind Source Separation

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KW - Contrast Function

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Liu H, Cheung YM. A Learning Framework for Blind Source Separation Using Generalized Eigenvalues. In Wang J, Liao XF, Yi Z, editors, Advances in Neural Networks - ISNN 2005: Second International Symposium on Neural Networks, Chongqing, China, May 30 - June 1, 2005, Proceedings, Part II. 1st ed. Berlin, Heidelberg: Springer. 2005. p. 472-477. (Lecture Notes in Computer Science). (Theoretical Computer Science and General Issues). (ISNN: International Symposium on Neural Networks). doi: 10.1007/11427445_77

A Learning Framework for Blind Source Separation Using Generalized Eigenvalues

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