Topology Selection for Self-Organizing Maps
Network: Computation in Neural Systems vol. 7, pp. 727-740
A topology-selection method for self-organizing maps (SOMs) based on
empirical Bayesian inference is presented.
This method is natural extension of the
hyperparameter-selection method presented earlier,
in which the SOM algorithm is regarded as an estimation algorithm
for a Gaussian mixture model with a Gaussian smoothing prior
on the centroid parameters,
and optimal hyperparameters are obtained by maximizing their evidence.
In the present paper,
comparisons between models with different topologies are made possible
by further specifying the prior of the centroid parameters
with an additional hyperparameter.
In addition, a fast hyperparameter-search algorithm
using the derivatives of evidence is presented.
The validity of the methods presented is confirmed by simulation experiments.