Neural network approach to classification using features extracted by mapping

A neural network approach to classification using features extracted by a mapping is presented. When the number of sample dimensions is much larger than the number of classes and no deviations are given but the means of classes, a mapping from class space to a new space whose dimensions are exactly equal to the number of classes is proposed. The vectors in the new space are considered as the feature vectors to be input to a neural network for classification. The property of the mapping that the separability of the original classification problem does not change is described. Simulation results for object recognition are presented.