Feature extraction by neural network nonlinear mapping for pattern classification

Feature extraction for exploratory data projection aims for data visualization by a projection of a high-dimensional space onto two or three-dimensional space, while feature extraction for classification generally requires more than two or three features. We study extraction of more than three features, using neural network (NN) implementation of Sammon's mapping to be applied for classification. The experiments reveal that Sammon's mapping, the multilayer perceptron (MLP) and the principal component analysis (PCA) based feature extractors yield similar classification performance. We investigate a random- and PCA-based initializations of Sammon's mapping. When the PCA is applied to initialize Sammon's projection, only one experiment is required and only a fraction of the training period is needed to achieve performance comparable with that of the random initialization. Furthermore, the PCA based initialization affords better human chromosome classification performance even when using a few eigenvectors.