Principal components applied to multi-layer perceptron learning

Addresses the problem of training a multi-layer perceptron neural network for use in statistical pattern recognition applications. In particular it suggests a method for training such a network which significantly reduces the number of iterations that usually accompanies the use of the back propagation learning algorithm. The use of principal component analysis is proposed, and an example is given that demonstrates significant improvements in convergence speed as well as the number of hidden layer neurons needed, while maintaining accuracy comparable to that of a conventional perceptron network trained using back propagation. The work is still of a preliminary nature, but the initial examples considered suggest the method has promise for statistical classification applications in which the pattern classes have normally distributed features