Robust self-organizing maps

The Self Organizing Map (SOM) model is an unsupervised
learning neural network that has been successfully applied as a data
mining tool. The advantages of the SOMs are that they preserve the
topology of the data space, they project high dimensional data to a
lower dimension representation scheme, and are able to find similarities
in the data.
However, the learning algorithm of the SOM is sensitive to the presence
of noise and outliers as we will show in this paper. Due to the influence
of the outliers in the learning process, some neurons (prototypes) of the
ordered map get located far from the majority of data, and therefore,
the network will not effectively represent the topological structure of the
data under study.
In this paper, we propose a variant to the learning algorithm that is
robust under the presence of outliers in the data by being resistant to
these deviations. We call this algorithm Robust SOM (RSOM). We will
illustrate our technique on synthetic and real data sets.