Title :
Flexible Kernel Independent Component Analysis Algorithm and its Local Stability on Feature Space
Author_Institution :
Fac. of Math. & Phys., Nanjing Univ. of Posts & Telecommun.
Abstract :
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
Keywords :
Gaussian distribution; independent component analysis; learning (artificial intelligence); nonlinear functions; Gaussian exponent; Stiefel manifold; flexible kernel independent component analysis algorithm; learning algorithm; nonlinear activation function; stability analysis; Cybernetics; Eigenvalues and eigenfunctions; Independent component analysis; Kernel; Machine learning; Machine learning algorithms; Manifolds; Mathematics; Matrix converters; Physics; Probability distribution; Stability analysis; Support vector machines; FKICA; activation function; feature space; local stability analysis; natural gradient;
Conference_Titel :
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location :
Dalian, China
Print_ISBN :
1-4244-0061-9
DOI :
10.1109/ICMLC.2006.259152
