SAMMONS MAPPING USING NEURAL NETWORKS - A COMPARISON

A well-known procedure for mapping data from a high-dimensional space onto a lower-dimensional one is Sammon's mapping. This algorithm prese rves as well as possible all inter-pattern distances. A major disadvan tage of the original algorithm lies in the fact that it is not easy to map hitherto unseen points. To overcome this problem, several methods have been proposed. in this paper, we aim to compare some approaches to implement this mapping on a neural network. (C) 1997 Elsevier Scien ce B.V.