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 ne...

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Bibliographic Details
Published in:Neural networks 1999-12, Vol.12 (10), p.1391-1397
Main Authors: Lai, P.L, Fyfe, C
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Canonical correlation analysis
Computer science
control theory
systems
Connectionism. Neural networks
Exact sciences and technology
Nonlinear correlations
Random dot stereograms
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Summary: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.
ISSN:0893-6080
1879-2782
DOI:10.1016/S0893-6080(99)00075-1