Abstract
This paper presents a novel independent component analysis algorithm that separates mixtures using serially updating geodesic method. The geodesic method is derived from the Stiefel manifold, and an on-line version of this method that can directly treat with the unwhitened observations is obtained. Simulation of artificial data as well as real biological data reveals that our proposed method has fast convergence.
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© 2006 Springer-Verlag Berlin Heidelberg
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Yu, T., Shao, HZ., Peng, QC. (2006). An ICA Learning Algorithm Utilizing Geodesic Approach. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_162
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DOI: https://doi.org/10.1007/11759966_162
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34439-1
Online ISBN: 978-3-540-34440-7
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