A Learning Framework for Blind Source Separation Using Generalized Eigenvalues

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.

• Type: Conference
• Year: 2005
• Where: ISNN
• Authors: Hailin Liu, Yiu-ming Cheung
• Keywords: Blind Source Separation, ISNN 2005, Typical Information-theoretical Approaches, Unknown Unmixing Matrix