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A neural implementation of canonical correlation analysis

โœ Scribed by P.L Lai; C Fyfe

Book ID
104349007
Publisher
Elsevier Science
Year
1999
Tongue
English
Weight
123 KB
Volume
12
Category
Article
ISSN
0893-6080

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โœฆ Synopsis

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.

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We review a recent neural implementation of Canonical Correlation Analysis and show, using ideas suggested by Ridge Regression, how to make the algorithm robust. The network is shown to operate on data sets which exhibit multicollinearity. We develop a second model which not only performs as well on