A comparative study of neural network based feature extraction paradigms Academic Article uri icon


  • Abstract The projection maps and derived classification accuracies of a neural network (NN) implementation of Sammon's mapping, an auto-associative NN (AANN) and a multilayer perceptron (MLP) feature extractor are compared with those of the conventional principal component analysis (PCA). Tested on five real-world databases, the MLP provides the highest classification accuracy at the cost of deforming the data structure, whereas the linear models preserve the structure but usually with inferior accuracy.

publication date

  • January 1, 1999