Training multilayer perceptrons by principal component analysis

We present a training algorithm for multilayer perceptrons which relates to the technique of principal component analysis. The latter is performed with respect to a correlation matrix which is computed from the example inputs and their target outputs. For large networks the novel procedure requires far fewer examples for good generalization than traditional on-line algorithms.