Canonical correlation analysis neural networks
We review a new method of performing canonical correlation analysis (CCA) with artificial neural networks. We have previously (1998, 1999) compared its capabilities with standard statistical methods on simple data sets such as an abstraction of random dot stereograms. In this paper, we show that this original rule is only one of a family of rules 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.