Abstract
Activation function is a crucial factor in independent component analysis (ICA) and the best one is the score function defined on the probability density function (pdf) of the source. However, in FastICA, the activation function has to be selected from several predefined choices according to the prior knowledge of the sources, and the problem of how to select or optimize activation function has not been solved yet. In this paper, self-adaptive FastICA is presented based on the generalized Gaussian model (GGM). By combining the optimization of the GGM parameter and that of the demixing vector, a general framework for self-adaptive FastICA is proposed. Convergence and stability of the proposed algorithm are also addressed. Simulation results show that self-adaptive FastICA is effective in parameter optimization and has better accuracy than traditional FastICA.
Supported by National Natural Science Foundation of China (30370416, 60303012, & 60234030).
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Wang, G., Xu, X., Hu, D. (2005). Self-adaptive FastICA Based on Generalized Gaussian Model. In: Wang, J., Liao, X., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427391_154
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DOI: https://doi.org/10.1007/11427391_154
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25912-1
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