Principal Independent Component Network for Dimensionality Reduction and Blind Source Separation
Zhenhua Guo · Shuju caiji yu chuli · 2004
Principal component analysis and principal component neural network generally use the index of the total variance interpreted by principal components to choose the adequate number of principal component. These approaches implicitly suppose that the system data are Gaussian distribution and may be inappropriate for the dimensionality reduction of the non-Gaussian data. Considering the dimensionality reduction and the blind source separation of the mixture data from non-Gaussian stochastic systems, a principal independent neural network based on second order Renyi entropy criterion is proposed. An approximation method for the computation of the Renyi entropy criterion and the corresponding gradient learning algorithm are given. Simulation example shows the effectiveness of the approach for the dimensionality reduction and its advantages of the blind source separation over general principle component analysis.