Title :
A Simple and Flexible Nonlinearty Approach to Independent Component Analysis
Author_Institution :
Dept. of Appl. Math., Dalian Nat. Univ.
Abstract :
A simple and flexible nonlinearity approach to independent component analysis is presented, which is able to blindly separate mixed super-Gaussian, Gaussian and sub-Gaussian sources. The parameter of the nonlinearity is estimated by representing it as a function of the kurtosis of sources. Further, the stability conditions for the proposed algorithm are analyzed to give a robust algorithm for independent component analysis. We show that this algorithm can interestingly be used to find hidden physiological processes inherent in gene expression experiments
Keywords :
Gaussian processes; independent component analysis; nonlinear systems; physiological models; flexible nonlinearity approach; gene expression experiments; hidden physiological processes; independent component analysis; mixed super-Gaussian sources; Algorithm design and analysis; Exponential distribution; Independent component analysis; Mathematics; Nonlinear equations; Parameter estimation; Robust stability; Robustness; Signal processing algorithms; Stability analysis;
Conference_Titel :
Neural Networks and Brain, 2005. ICNN&B '05. International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-9422-4
DOI :
10.1109/ICNNB.2005.1615011
