Superposition-based order analysis in self-organizing maps

Topology preservation, the central feature of SOM, proves to be difficult to define and quantify. We propose new criteria and measurements for order preservation, based on the superposition of neuron cells in the input space. As an application, we establish a taxonomy of topological errors and disorder states and use it for error detection in 2-dimensional SOM during the first learning epochs.