Input feature extraction for multilayered perceptrons using supervised principal component analysis

A method is proposed for constructing salient features from a set of features that are given as input to a feedforward neural network used for supervised learning. Combinations of the original features are formed that maximize the sensitivity of the network's outputs with respect to variations of its inputs. The method exhibits some similarity to Principal Component Analysis, but also takes into account supervised character of the learning task. It is applied to classification problems leading to improved generalization ability originating from the alleviation of the curse of dimensionality problem.