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Developments of the generative topographic mapping

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)203-224
Number of pages22
JournalNeurocomputing
Volume21
Issue number1–3
DOIs
Publication statusPublished - Nov 1998

Abstract

The generative topographic mapping (GTM) model was introduced by Bishop et al. (1998, Neural Comput. 10(1), 215–234) as a probabilistic re-formulation of the self-organizing map (SOM). It offers a number of advantages compared with the standard SOM, and has already been used in a variety of applications. In this paper we report on several extensions of the GTM, including an incremental version of the EM algorithm for estimating the model parameters, the use of local subspace models, extensions to mixed discrete and continuous data, semi-linear models which permit the use of high-dimensional manifolds whilst avoiding computational intractability, Bayesian inference applied to hyper-parameters, and an alternative framework for the GTM based on Gaussian processes. All of these developments directly exploit the probabilistic structure of the GTM, thereby allowing the underlying modelling assumptions to be made explicit. They also highlight the advantages of adopting a consistent probabilistic framework for the formulation of pattern recognition algorithms.

    Research areas

  • Bayesian inference

ID: 21957226