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. © Springer-Verlag Berlin Heidelberg 2006.
Cite
CITATION STYLE
Yu, T., Shao, H. Z., & Peng, Q. C. (2006). An ICA learning algorithm utilizing geodesic approach. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3971 LNCS, pp. 1103–1108). Springer Verlag. https://doi.org/10.1007/11759966_162
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