Visualization of multidimensional data taking into account the learning flow of the self organizing neural network

Abstract

In the paper we discuss the visualization of multidimensional vectors taking into account the learning flow of the self organizing neural network. A new algorithm realizing a combination of the self-organizing map (SOM) and Sammon's mapping has been proposed. It takes into account the intermediate learning results of the SOM. The experiments showed that the algorithm gives lower average projection errors compared with a consequent application of the SOM and Sammon's mapping.