The Self-Organising Hierarchical Variance Map

The self-organising hierarchical variance map (SOHVM), a novel unsupervised clustering technique is proposed. Based on both Kohonen and Hebhian principles of self-organisation, the algorithm works to dynamically construct a topology preserved mapping of dominant prototype clusters from within an unknown data source. Each neuron in the network consists of a dual memory element that tracks information regarding a discovered prototype. In addition to position, Hebbian based maximum eigenfilters (HME) simultaneously estimate the maximal variance of local data. Competitive Hebhian learning (CHL) Is used to dynamically associate prototypes such that an accurate topology is maintained throughout the discovery process. Knowledge may then be progressively imparted to the network through appropriate neighbouring memory elements. Vigilance is assessed via interplay between local variances such that more informed decisions control and naturally limit network growth. The approach is closely related to self-organizing tree maps (SOTM), growing neural gas (GNG) and their variants.