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 un-mixing 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.
This work was supported by the Research Grant Council of Hong Kong SAR under Project HKBU 2156/04E.
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References
Hyvarinem, A., Oja, E.: Independent Component Analysis: Algorithms and Applications. Neural Networks 13, 411–430 (2000)
Bell, A.J., Sejnowski, T.J.: An Information Approach to Blind Separation and Blind Deconvolution. Neural Computation 7, 1129–1159 (1995)
Girolami, M., Fyfe, C.: Extraction of Independent Signal Sources Using a Deflationary Exploratory Projection Pursuit Network with Lateral Inhibition. IEE Proceedings on Vision, Image and Signal Processing Journal 14, 299–306 (1997)
Hyvarinem, A., Oja, E.: Independent Component Analysis: Algorithms and Applications. Neural Networks 13, 411–430 (2000)
Pham, D.T., Garrat, P., Jutten, C.: Separation of AMixture of Independent Sources Through a Maximum Likelihood Approach. In: Proceedings of EUSIPCO, pp. 771–774 (1992)
Stone, J.V.: Blind Source Separation Using Temporal Predictability. Neural Computation 13, 1559–1574 (2001)
Cheung, Y.M., Liu, H.L.: A New Approach to Blind Source Separation with Global Optimal Property. In: Proceedings of the IASTED International Conference of Neural Networks and Computational Intelligence, Grindelwald, Switzerland, pp. 137–141 (2004)
Parra, L., Sajda, P.: Blind Source Separation via Generalized Eigenvalue Decomposition. Journal of Machine Learning Research 4, 1261–1269 (2003)
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© 2005 Springer-Verlag Berlin Heidelberg
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Liu, H., Cheung, Y. (2005). A Learning Framework for Blind Source Separation Using Generalized Eigenvalues. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_77
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DOI: https://doi.org/10.1007/11427445_77
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
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