Multiple Canonical Correlation with Neural

We review [4] a new method of performing Canonical Correlation Analysis with Artificial Neural Networks. We demonstrate its capability on a real data set where the results are compared with those achieved with standard statistical tools. In this paper, we extend the method by implementing a very precise set of constraints which allow multiple correlations to be found at once. We demonstrate the network's capabilities on artificial data and on the standard random dot stereogram data set