The kernel self-organising map

Abstract

We review a recently-developed method of performing k-means clustering in a high-dimensional feature space and extend it to give the resultant mapping topology-preserving properties. We show the results of the new algorithm on the standard data set, on random numbers drawn uniformly from [0,1)/sup 2/ and on the Olivetti database of faces. The new algorithm converges extremely quickly.