On blind source separation using generalized eigenvalues with a new metric

Authors:
Hai-lin Liu;Yiu-ming Cheung
Affiliations:
Faculty of Applied Mathematics, Guangdong University of Technology, China;Department of Computer Science, Hong Kong Baptist University, Hong Kong, SAR, China
Venue:
Neurocomputing
Year:
2008

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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.