Learning embedded data structure with self-organizing maps

Abstract

Self-Organizing Map (SOM) is undoubtedly one of the most famous and successful artificial neural network approaches. Since the SOM is related with the Vector Quantization learning process, minimizing error quantization and maximizing topology preservation can be concurrent tasks. Besides, even with some metrics, sometimes the analysis of the map results depends on the user and poses an additional difficulty when the user deals with high dimensional data. This work discusses a proposal of relocating the voted map units after the training phase in order to minimize the quantization error and evaluate the impact in the topology preservation. The idea is to enhance the visualization of embedded data structure from input samples using the SOM. © 2013 Springer-Verlag.