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Flexible Kernel Independent Component Analysis Algorithm and its Local Stability on Feature Space

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1 Author(s)
Lei Li ; Faculty of Mathematics and Physics, Nanjing University of Posts & Telecommunications, 210003, Nanjing, P. R. China. E-MAIL: lileinjupt@163.com

In this paper a novel flexible kernel independent component analysis (FKICA) algorithm is defined and its local stability on feature space is discussed. In the FKICA algorithm, the shape of nonlinear activation function in the learning algorithm varies depending on the Gaussian exponent, which is properly selected according to the kurtosis of estimated source in feature space. In the framework of the natural gradient in Stiefel manifold, the FKICA algorithm is visited and some results about its local stability analysis are presented

Published in:

2006 International Conference on Machine Learning and Cybernetics

Date of Conference:

13-16 Aug. 2006