Robust growing hierarchical self organizing map

Abstract

The Growing Hierarchical Self Organizing Map (GHSOM) was introduced as a dynamical neural network model that adapts its architecture during its unsupervised training process to represents the hierarchical relation of the data. However, the dynamical algorithm of the GHSOM is sensitive to the presence of noise and outliers, and the model will no longer preserve the topology of the data space as we will show in this paper. The outliers introduce an influence to the GHSOM model during the training process by locating prototypes far from the majority of data and generating maps for few samples data. Therefore, the network will not effectively represent the topological structure of the data under study. In this paper, we propose a variant to the GHSOM algorithm that is robust under the presence of outliers in the data by being resistant to these deviations. We call this algorithm Robust GHSOM (RGHSOM). We will illustrate our technique on synthetic and real data sets. © Springer-Verlag Berlin Heidelberg 2005.