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 |
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Pages (from-to) | 472-477 |
Number of pages | 6 |
Journal | Lecture Notes in Computer Science |
Volume | 3497 |
Issue number | II |
DOIs | |
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)