Robustness analysis of the neural gas learning algorithm

The Neural Gas (NG) is a Vector Quantization technique where a set
of prototypes self organize to represent the topology structure of the data. The
learning algorithm of the Neural Gas consists in the estimation of the prototypes
location in the feature space based in the stochastic gradient descent of an Energy
function. In this paper we show that when deviations from idealized distribution
function assumptions occur, the behavior of the Neural Gas model can be drastically affected and will not preserve the topology of the feature space as desired.
In particular, we show that the learning algorithm of the NG is sensitive to the
presence of outliers due to their influence over the adaptation step.
We incorporate a robust strategy to the learning algorithm based on
M-estimators where the influence of outlying observations are bounded. Finally
we make a comparative study of several estimators where we show the superior
performance of our proposed method over the original NG, in static data clustering tasks on both synthetic and real data sets.