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) 

作者机构：Guilin Institute of Electronic Technology Guilin Key Laboratory of Radar Signal Processing Xidian Univ. Xi’an 

出 版 物：《Journal of Electronics(China)》 (电子科学学刊（英文版）)

年 卷 期：2000年第17卷第3期

页      面：270-278页

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

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

主　　题：Neural networks Principal component analysis Auto-association Recursive least squares(RLS) learning rule 

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 *** 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.