A learning framework for blind source separation using generalized eigenvalues

Authors:
Hailin Liu;Yiuming Cheung
Affiliations:
Faculty of Applied Mathematics, Guangdong University of Technology, China;Department of Computer Science, Hong Kong Baptist University, Hong Kong, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Year:
2005

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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 timeconsuming 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.