Canonical Correlation Analysis Neural Networks

被引：0

作者：

Fyfe, C ^{[1
]}

Lai, PL ^{[1
]}

机构：

[1] Univ Paisley, Dept Comp & Imformat Syst, Appl Computat Intelligence Res Unit, Paisley PA1 2BE, Renfrew, Scotland

来源：

15TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 2, PROCEEDINGS: PATTERN RECOGNITION AND NEURAL NETWORKS | 2000年

关键词：

Canonical Correlation Analysis;

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：

We review a new method of performing Canonical Correlation Analysis (CCA) with Artificial Neural Networks. We have previously [4, 5] compared its capabilities with standard statistical methods on simple data sets such as an abstraction of random dot stereograms [2]. In this paper, we show that this original rule is only one of a family of rules all of which use Hebbian and anti-Hebbian learning to find correlations between data sets: we derive slightly different rules from Becker's information theoretic criteria and from probabilistic assumptions. We then derive a robust version of this last rule and then compare the effectiveness of these rules on a standard data set.

引用

页码：977 / 980

页数：4