On blind source separation using generalized eigenvalues with a new metric

Hai lin Liu; Yiu Ming CHEUNG

doi:10.1016/j.neucom.2007.02.004

On blind source separation using generalized eigenvalues with a new metric

Hai lin Liu, Yiu Ming CHEUNG^*

^*Corresponding author for this work

Department of Computer Science

Research output: Contribution to journal › Journal article › peer-review

6 Citations (Scopus)

Abstract

Following the seminal work of Stone [Independent Component Analysis, The MIT Press, Cambridge, 2004], this paper presents a new metric for blind source separation (BSS). It is proved that the metric value of any linear combination of source signals is less than the largest one of sources under a loose condition. Further, the global optimization of this new metric is achieved by formulating it as a generalized eigenvalue (GE) problem. Subsequently, we give out a fast BSS algorithm. Moreover, we analyze the solution properties of ill-posed BSS, and further show that the proposed algorithm is applicable to such a case as well. The numerical simulations demonstrate the efficacy of our algorithm.

Original language	English
Pages (from-to)	973-982
Number of pages	10
Journal	Neurocomputing
Volume	71
Issue number	4-6
DOIs	https://doi.org/10.1016/j.neucom.2007.02.004
Publication status	Published - Jan 2008

User-Defined Keywords

Blind source separation
Generalized eigenvalue problem
Ill-posed ICA
Independent component analysis
Optimal solution

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10.1016/j.neucom.2007.02.004

Cite this

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title = "On blind source separation using generalized eigenvalues with a new metric",

abstract = "Following the seminal work of Stone [Independent Component Analysis, The MIT Press, Cambridge, 2004], this paper presents a new metric for blind source separation (BSS). It is proved that the metric value of any linear combination of source signals is less than the largest one of sources under a loose condition. Further, the global optimization of this new metric is achieved by formulating it as a generalized eigenvalue (GE) problem. Subsequently, we give out a fast BSS algorithm. Moreover, we analyze the solution properties of ill-posed BSS, and further show that the proposed algorithm is applicable to such a case as well. The numerical simulations demonstrate the efficacy of our algorithm.",

keywords = "Blind source separation, Generalized eigenvalue problem, Ill-posed ICA, Independent component analysis, Optimal solution",

author = "Liu, {Hai lin} and CHEUNG, {Yiu Ming}",

note = "Funding Information: The work described in this paper was supported by Faculty Research Grant of Hong Kong Baptist University with Project Number: FRG/05-06/II-42, FRG/06-07/II-07, and by the Research Grant Council of Hong Kong SAR under Projects HKBU 2156/04E and HKBU 210306. ",

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AU - CHEUNG, Yiu Ming

N1 - Funding Information: The work described in this paper was supported by Faculty Research Grant of Hong Kong Baptist University with Project Number: FRG/05-06/II-42, FRG/06-07/II-07, and by the Research Grant Council of Hong Kong SAR under Projects HKBU 2156/04E and HKBU 210306.

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N2 - Following the seminal work of Stone [Independent Component Analysis, The MIT Press, Cambridge, 2004], this paper presents a new metric for blind source separation (BSS). It is proved that the metric value of any linear combination of source signals is less than the largest one of sources under a loose condition. Further, the global optimization of this new metric is achieved by formulating it as a generalized eigenvalue (GE) problem. Subsequently, we give out a fast BSS algorithm. Moreover, we analyze the solution properties of ill-posed BSS, and further show that the proposed algorithm is applicable to such a case as well. The numerical simulations demonstrate the efficacy of our algorithm.

AB - Following the seminal work of Stone [Independent Component Analysis, The MIT Press, Cambridge, 2004], this paper presents a new metric for blind source separation (BSS). It is proved that the metric value of any linear combination of source signals is less than the largest one of sources under a loose condition. Further, the global optimization of this new metric is achieved by formulating it as a generalized eigenvalue (GE) problem. Subsequently, we give out a fast BSS algorithm. Moreover, we analyze the solution properties of ill-posed BSS, and further show that the proposed algorithm is applicable to such a case as well. The numerical simulations demonstrate the efficacy of our algorithm.

KW - Blind source separation

KW - Generalized eigenvalue problem

KW - Ill-posed ICA

KW - Independent component analysis

KW - Optimal solution

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U2 - 10.1016/j.neucom.2007.02.004

DO - 10.1016/j.neucom.2007.02.004

M3 - Journal article

AN - SCOPUS:38649117322

SN - 0925-2312

VL - 71

SP - 973

EP - 982

JO - Neurocomputing

JF - Neurocomputing

IS - 4-6

ER -

On blind source separation using generalized eigenvalues with a new metric

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