Discriminant analysis neural networks

An artificial neural network and a supervised self-organizing learning algorithm for multivariate linear discriminant analysis are proposed. The precision of the neural computation is shown to be high enough for feature selection and projection purposes. A nonlinear discriminant analysis network (supervised nonlinear projection method) based on the multilayer feedforward network is also suggested. A comparative study of the principal component analysis network, linear discriminant analysis network, and nonlinear discriminant analysis network based on three criteria on various data sets is provided. A significance advantage of these neural networks over conventional approaches is their plasticity, which allows the networks to adapt themselves to new input data.<<ETX>>