A Convergent Algorithm for PCA Neural Network
一种全局收敛的PCA神经网络学习算法

Principal component analysis (PCA)is one of the most general-purpose feature extraction methods. For processing the huge data sets, a variety of learning algorithm for PCA has been proposed. However, traditional algorithms will either divergence or convergence very slowly. Based on the CRLS neural network,a novel convergence algorithm is proposed and the fact that the weight vector will converge to the largest eigenvector is also proved. Finally ,simulation results are also included to illustrate the accuracy of this new algorithm.