Sanger's like systems for generalized principal and minor component analysis

被引：0

作者：

Hasan, Mohammed A. ^{[1
]}

机构：

[1] Univ Minnesota, Dept Elect & Comp Engn, Duluth, MN 55812 USA

来源：

2006 IEEE SENSOR ARRAY AND MULTICHANNEL SIGNAL PROCESSING WORKSHOP PROCEEDINGS, VOLS 1 AND 2 | 2006年

关键词：

dynamical systems; global stability; generalized eigenvalue problem; principal component analysis; minor component analysis; Oja's learning rule; Sanger's learning rule;

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 generalizations of Sanger's learning rule for nondefinite matrices are explored. It is shown that the left and right principal components of any matrix can be computed so that these components upper triangulize the original matrix. We also modified the original Sanger's system to obtain new dynamical systems with a larger domain of attraction. Stability analysis for several Sanger's type systems for the standard and generalized principal, and minor component analyzers applied to nonsymmetric matrices is developed.

引用

页码：425 / 429

页数：5

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