A neural implementation of canonical correlation analysis

We derive a new method of performing Canonical Correlation Analysis with Artificial Neural Networks. We demonstrate the network's capabilities on artificial data and then compare its effectiveness with that of a standard statistical method on real data. We demonstrate the capabilities of the network in two situations where standard statistical techniques are not effective: where we have correlations stretching over three datasets and where the maximum nonlinear correlation is greater than any linear correlation. The network is also applied to Becker's (Network: Computation in Neural Systems, 1996, 7:7-31) random dot stereogram data and shown to be extremely effective at detecting shift information. (C) 1999 Elsevier Science Ltd. All rights reserved.