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, 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.
