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JACIII Vol.17 No.4 pp. 473-479
doi: 10.20965/jaciii.2013.p0473
(2013)

Paper:

Constructing Generative Topographic Mapping by Variational Bayes with ARD Hierarchical Prior

Nobuhiko Yamaguchi

Graduate School of Science and Engineering, Saga University, 1 Honjo, Saga 840-8502, Japan

Received:
January 11, 2013
Accepted:
April 2, 2013
Published:
July 20, 2013
Keywords:
generative topographic mapping, data visualization, variational Bayes, automatic relevance determination
Abstract

Generative Topographic Mapping (GTM) is a nonlinear latent variable model introduced as a data visualization technique by Bishop et al. In this paper, we focus on variational Bayesian inference in GTM. Variational Bayesian GTM, first proposed by Olier et al., uses a single regularization term and regularization parameter to avoid overfitting and therefore cannot be used to control the degree of regularization locally. To overcome this problem, we propose variational Bayesian inference with Automatic Relevance Determination (ARD) hierarchical prior for use with GTM. The proposed model uses multiple regularization parameters and therefore can be used to control the degree of regularization in local areas of data space individually. Several experiments show that GTM that we propose provides better visualization than conventional GTM approaches.

Cite this article as:
Nobuhiko Yamaguchi, “Constructing Generative Topographic Mapping by Variational Bayes with ARD Hierarchical Prior,” J. Adv. Comput. Intell. Intell. Inform., Vol.17, No.4, pp. 473-479, 2013.
Data files:
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