Two efficient connectionist schemes for structure preserving dimensionality 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 an MLP. Method 2 uses Kohonen's self-organizing feature map (SOFM) 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 on the data sets tried.