Canonical correlation analysis using a neural network

Kay, J. (1992) Canonical correlation analysis using a neural network. In: Dodge, Y. and Whittaker, J. (eds.) Computational Statistics Volume II: Proceedings of the 10th Symposium on Computational Statistics, COMPSTAT, Neuchâtel, Switzerland, August 1992. Physica-Verlag: Heidelberg, pp. 305-308. ISBN 9783642486807 (doi: 10.1007/978-3-642-48678-4_38)

Full text not currently available from Enlighten.


We introduce an artificial neural network which performs a canonical correlation analysis. Our approach is to develop a stochastic algorithm which converges to the stationarity equations for the determination of the canonical variables and the canonical correlations. Although the intrinsic algorithm is not local in the sense that the computation at a particular unit involves the values of distant units in the network, it is possible to employ a simple recursive from of communication between neighbouring nodes in order to achieve local computation. Some non-linear possibilities are discussed briefly.

Item Type:Book Sections
Glasgow Author(s) Enlighten ID:Kay, Dr James
Authors: Kay, J.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics

University Staff: Request a correction | Enlighten Editors: Update this record