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: Contribution to journal › Conference article › 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
Pages (from-to)	472-477
Number of pages	6
Journal	Lecture Notes in Computer Science
Volume	3497
Issue number	II
DOIs	https://doi.org/10.1007/11427445_77
Publication status	Published - 2005
Event	Second International Symposium on Neural Networks: Advances in Neural Networks - ISNN 2005 - Chongqing, China Duration: 30 May 2005 → 1 Jun 2005

Scopus Subject Areas

Theoretical Computer Science
Computer Science(all)

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10.1007/11427445_77

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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.",

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

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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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M3 - Conference article

AN - SCOPUS:24944521606

SN - 0302-9743

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T2 - Second International Symposium on Neural Networks: Advances in Neural Networks - ISNN 2005

Y2 - 30 May 2005 through 1 June 2005

ER -

A learning framework for blind source separation using generalized Eigenvalues

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