Abstract
We revisit the one-unit gradient ICA algorithm derived from the kurtosis function. By carefully studying properties of the stationary points of the discrete-time one-unit gradient ICA algorithm, with suitable condition on the learning rate, convergence can be proved. The condition on the learning rate helps alleviate the guesswork that accompanies the problem of choosing suitable learning rate in practical computation. These results may be useful to extract independent source signals on-line.
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Ye, M., Li, X., Yang, C., Gao, Z. (2006). Convergence Analysis of a Discrete-Time Single-Unit Gradient ICA Algorithm. 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_168
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DOI: https://doi.org/10.1007/11759966_168
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34439-1
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