Abstract
Obtaining a low complexity activation function and an online sub-block learning for non-gaussian mixtures are presented in this paper. The paper deals with independent component analysis with mutual information as a cost function. First, we propose a low complexity activation function for non-gaussian mixtures, and then an online sub-block learning for stationary mixture is introduced. The size of the sub-blocks is larger than the maximal frequency F max of the principal component of the original signals. Experimental results proved that the proposed activation function and the online sub-block learning method are more efficient in terms of computational complexity as well as in terms of learning ability.
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© 2003 Springer-Verlag Berlin Heidelberg
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Chinnasarn, K., Lursinsap, C., Palade, V. (2003). Low Complexity Functions for Stationary Independent Component Mixtures. In: Palade, V., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2003. Lecture Notes in Computer Science(), vol 2773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45224-9_90
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DOI: https://doi.org/10.1007/978-3-540-45224-9_90
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40803-1
Online ISBN: 978-3-540-45224-9
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