Canonical correlation analysis using artificial neural networks
We derive a new method of performing Canonical Correlation Analysis with Artiicial Neural Networks. We demonstrate its capability on a simple artiicial data set and then on a real data set where the results are compared with those achieved with standard statistical tools. We then extend the method to deal with a situation where there are two equal competing correlations within the datasets and show that this extension is eeective on the previous data sets.