Learning of Mahalanobis Discriminant Functions by a Neural Network

Authors:
Yoshifusa Ito;Hiroyuki Izumi;Cidambi Srinivasan
Affiliations:
School of Medicine, Aichi Medical University, Nagakute, Japan 480-1195;Department of Policy Science, Aichi-Gakuin University, Nisshin, Japan 470-0195;Department of Statistics, University of Kentucky, Lexington, USA 40506
Venue:
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part I
Year:
2009

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Abstract

It is known that a neural network can learn a Bayesian discriminant function. Ito et al. (2006) has pointed out that if the inner potential of the output unit of the network is shifted by a constant, the output becomes a Mahalanobis discriminant function. However, it was a heavy task for the network to calculate the constant. Here, we propose a new algorithm with which the network can estimate the constant easily. This method can be extended to higher dimensional classificasions problems without much effort.