FAST RECURSIVE LEAST SQUARES LEARNING ALGORITHM FOR PRINCIPAL COMPONENT ANALYSIS  

作　　者：Ouyang Shan Bao Zheng Liao Guisheng(Guilin Institute of Electronic Technology, Guilin 541004)(Key Laboratory of Radar Signal Processing, Xidian Univ., Xi’an 710071) 

出　　处：《Journal of Electronics(China)》2000年第3期270-278,共9页电子科学学刊（英文版）

基　　金：Supported by the National Natural Science Foundation of China;the Science foundation of Guangxi Educational Administration

摘　　要：Based on the least-square minimization a computationally efficient learning algorithm for the Principal Component Analysis(PCA) is derived. The dual learning rate parameters are adaptively introduced to make the proposed algorithm providing the capability of the fast convergence and high accuracy for extracting all the principal components. It is shown that all the information needed for PCA can be completely represented by the unnormalized weight vector which is updated based only on the corresponding neuron input-output product. The convergence performance of the proposed algorithm is briefly analyzed.The relation between Oja’s rule and the least squares learning rule is also established. Finally, a simulation example is given to illustrate the effectiveness of this algorithm for PCA.Based on the least-square minimization a computationally efficient learning algorithm for the Principal Component Analysis(PCA) is derived. The dual learning rate parameters are adaptively introduced to make the proposed algorithm providing the capability of the fast convergence and high accuracy for extracting all the principal components. It is shown that all the information needed for PCA can be completely represented by the unnormalized weight vector which is updated based only on the corresponding neuron input-output product. The convergence performance of the proposed algorithm is briefly analyzed.The relation between Oja's rule and the least squares learning rule is also established. Finally, a simulation example is given to illustrate the effectiveness of this algorithm for PCA.

关 键 词：Neural networks Principal component analysis Auto-association RECURSIVE least squares(RLS) learning RULE 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]