Novel algorithm for eliminating folding effect in standard SOM
Kirmene Marzouki, Takeshi Yamakawa · The European Symposium on Artificial Neural Networks · 2005
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, as the original input manifold can be complicated with an inherent dimension larger than that of the feature map, the dimension reduction in SOM can be too drastic, generating a folded feature map. In order to eliminate this phenomenon, we extend the neighborhood concept to a new set of sub-neighbors, other than those introduced by Kohonen. The modified algorithm was applied to color classification and performed very well in comparison with the traditional SOM.