A robust and flexible model of hierarchical self-organizing maps for non-stationary environments

In this paper we extend the Hierarchical Self Organizing Maps model (HSOM ) to address the problem of learning topological drift under non stationary and noisy environments. The new model combines the capabilities of robustness against noise and, at the same time, the flexibility to adapt to the changing environment. We call this model RoFlex-HSOM. The RoFlex-HSOM model consists in a hierarchical tree structure of growing self organizing maps that adapts its architecture based on the data. The model preserves the topology mapping from the high-dimensional time dependent input space onto a neuron position in a low-dimensional hierarchical output space grid. Furthermore the RoFlex-HSOM algorithm has the plasticity to track and adapt to the topological drift, it gradually forgets (but no catastrophically) previous learned patterns and it is resistant to the presence of noise. We empirically show the capabilities of our model with experimental results using synthetic sequential data sets and the “El Niño” real world data.