Infinity Norm Based Neural Network Algorithm for Principal Component Analysis

被引：0
|
作者：
Liu, Lijun [1 ]
Xing, Hongjie [2 ]
Nan, Dong [3 ]
机构：
[1] Dalian Nationalities Univ, Dept Math, Dalian 116600, Peoples R China
[2] Hebei Univ, Coll Math & Comp Sci, Baoding 071002, Peoples R China
[3] Beijing Univ Technol, Coll Appl Sci, Beijing 100022, Peoples R China
来源：
2008 IEEE CONFERENCE ON CYBERNETICS AND INTELLIGENT SYSTEMS, VOLS 1 AND 2 | 2008年
关键词：
Principal component analysis; Eigenvalue; Neural Network; Convergence;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper a simple infinity norm based neural network algorithm for estimation of the principal component is developed. It seems to be especially useful in applications with changing environment, where the learning process has to be repeated in on-line manner. Theoretical analysis shows the weight vector converges to the principal eigenvector asymptotically. In comparison with the existing algorithms, numerical simulation shows that the proposed algorithm demonstrates fast convergence and robustness for a slightly noisy Gaussian samples with some points having large magnitude and angle with respect to the principal direction.
引用
收藏
页码：310 / +
页数：3
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