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 data sets 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.
Lai
P L.
PL
Department of Computing and Information Systems, Applied Computational Intelligence Research Unit, The University of Paisley, Paisley, UK
Fyfe
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