Bayesian Sampling and Ensemble Learning in Generative Topographic Mapping

Akio Utsugi

Neural Processing Letters vol. 12, no. 3, pp. 277-290.

Abstract:

Generative topographic mapping (GTM) is a statistical model to extract a hidden smooth manifold from data, like the self-organizing map (SOM). 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 abundant data. 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 is suggested.

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