Improving the efficiency of Sammon's nonlinear mapping by using clustering archetypes

Sammon's nonlinear mapping technique is discussed and certain inefficiencies are highlighted. It is shown how the algorithm can be streamlined by using appropriately chosen cluster archetypes. This results in reductions in storage requirements and in execution time, the latter by at least an order of magnitude.