Bayesian Sampling and Ensemble Learning in Generative Topographic Mapping
Neural Processing Letters vol. 12, no. 3, pp. 277-290.
Generative topographic mapping (GTM) is a statistical model
to extract a hidden smooth manifold from data, like the self-organizing
Although a deterministic search algorithm for the hyperparameters
regulating the smoothness of the manifold
has been proposed previously,
it is based on approximations that are valid only on
Thus, it often fails to obtain suitable estimates on small data.
In this paper, to improve the hyperparameter search in GTM,
we construct a Gibbs sampler on the model,
which generates random sample series following the
posteriors on the hyperparameters.
Reliable estimates are obtained from the samples.
In addition, we obtain
another deterministic algorithm using the ensemble learning.
From the result of an experimental comparison of these algorithms,
an efficient method for reliable estimation in GTM