Resolution enhancement for the ViSOM
Hujun Yin · Research Explorer (The University of Manchester) · 2003
The topology perserving self-organising maps (SOM) has become a powerful and useful tool for visualising high dimensional data. However it requires a colouring scheme to imprint the interneuron distances on the map. The recently proposed Visualisation induced SOM (ViSOM) is able to directly preserve the distance information, as well as topology, on the map. It has been proved to be a discrete principal curve/ surface. The resolution of the map is inversely proportional to the size of the grid. Large maps require more computation thus long training time. This paper proposes two simple methods for enhancing the resolution of the ViSOM so avoiding using large maps. The first interpolates a trained map, while the second incorporates local linear projections into the projection step when mapping the data on a trained map. Experiments and results are given to demonstrate the advantages.