FLEXIBLE KERNEL INDEPENDENT COMPONENT ANALYSIS ALGORITHM AND ITS LOCAL STABILITY ON FEATURE SPACE

摘要：

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.

关键词： FKICA local stability analysis activation function natural gradient feature space

作者: LEI LI

作者单位: Faculty of Mathematics and Physics, Nanjing University of Posts & Telecommunications, 210003, Nanjing P.R.China

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 2990-2994

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）