Adaptive learning rate control for "neural gas principal component analysis"

We propose a novel algorithm for adaptive learning rate control for Gaussian mixture models of the NGPCA type. The core idea is to introduce a unit–specific learning rate which is adjusted automatically depending on the match between the local principal component analysis of each unit (interpreted as Gaussian distribution) and the empirical distribution within the unit’s data partition. In contrast to fixed annealing schemes for the learning rate, the novel algorithm is applicable to real online learning. Two experimental studies are presented which demonstrate this important property and the general performance of this algorithm.