Abstract
The purpose of this paper is to develop two novel unified parametric and non-parametric Independent Component Analysis (ICA) algorithms, which enable to separate arbitrary sources including symmetric and asymmetric distributions with self-adaptive score functions. They are derived from the parameterized asymmetric generalized Gaussian density (AGGD) model and GGD kernel based k-nearest neighbor (KNN) non-parametric estimation. The parameters of the score function in the algorithms are been chosen adaptively by estimating the high order statistics of the observed signals and GGD kernel estimation based non-parametric method. Compared with conventional ICA algorithms, the two given methods can separate a wide range of source signals using only one unified density model. Simulations confirm the effectiveness and performance of the proposed algorithm.
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© 2006 Springer-Verlag Berlin Heidelberg
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Wang, F., Li, H., Li, R., Yu, S. (2006). Unified Parametric and Non-parametric ICA Algorithm for Arbitrary Sources. 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_165
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DOI: https://doi.org/10.1007/11759966_165
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34439-1
Online ISBN: 978-3-540-34440-7
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