A Probabilistic Derivation of Canonical Correlation Analysis
نویسنده
چکیده
We review a new method of performing Canonical Correlation Analysis (CCA) with Artificial Neural Networks. We have previously [4, 3] compared its capabilities with standard statistical methods on simple data sets where the maximum correlations are given by linear filters. In this paper, we re-derive the learning rules from a probabilistic perspective and then by use of a specific prior on the weights, simplify the algorithm. We demonstrate its capabilities on a standard problem [2]which is an abstraction of the Random Dot Stereogram matching problem and show how a second layer network using Factor Analysis can be used to combine the results of the CCA network to obtain higher order information.
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