Kernel and Nonlinear Canonical Correlation Analysis

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Venue:
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 4 - Volume 4
Year:
2000

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Abstract

We have previously [4] derived a neural network implementation of the statistical technique of Canonical Correlation Analysis (CCA). We extend this to nonlinear CCA either by adding a non-linearity to our neural method or by nonlinearly transforming the data to a feature space and then performing linear CCA in this feature space. We give comparative results on both artificial and real data sets.