Novel Learning Algorithm Aiming at Generating a Unique Units Distribution in Standard SOM

Abstract

Self-organizing maps, SOMs, are a data visualization technique developed to reduce the dimensions of data through the use of self-organizing neural networks. However, one of the limitations of Self Organizing Maps algorithm, is that every SOM is different and finds different similarities among the sample vectors each time the initial conditions are changed.

In this paper, we propose a modification of the SOM basic algorithm in order to make the resulted mapping invariant to the initial conditions. We extend the neighborhood concept to processing units, selected in a fashionable manner, other than those commonly selected relatively to the immediate surroundings of the best matching unit. We also introduce a new learning function for the newly introduced neighbors.

The modified algorithm was tested on a color classification application and performed very well in comparison with the traditional SOM.