Two efficient connectionist schemes for structure preservingdimensionality reduction

We propose two neural net based methods for structure preserving dimensionality reduction. Method 1 selects a small representative sample and applies Sammon's method to project it. This projected data set is then used to train a multilayer perceptron (MLP). Method 2 uses Kohonen's self-organizing feature map to generate a small set of prototypes which is then projected by Sammon's method. This projected data set is then used to train an MLP. Both schemes are quite effective in terms of computation time and quality of output, and both outperform methods of Jain and Mao (1992, 1995) on the data sets tried.