On the speed of training networks with correlated features

The learning speed of the adaptive linear combiner is determined by the condition number of the input correlation matrix of the training data. With known properties of such correlation matrices, it is shown that increasing the dimensionality of the feature space of an adaptive linear combiner will never increase its learning speed. In fact, the learning speed will at best remain equal, but will deteriorate in most cases.<<ETX>>