GTM: The generative topographic mapping

Christopher M Bishop, Markus Svensén, Christopher KI Williams

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Latent variable models represent the probability density of data in a space of several dimensions in terms of a smaller number of latent, or hidden, variables. A familiar example is factor analysis, which is based on a linear transformation between the latent space and the data space. In this article, we introduce a form of nonlinear latent variable model called the generative topographic mapping, for which the parameters of the model can be determined using the expectation-maximization algorithm. GTM provides a principled alternative to the widely used self-organizing map (SOM) of Kohonen (1982) and overcomes most of the significant limitations of the SOM. We demonstrate the performance of the GTM algorithm on a toy problem and on simulated data from flow diagnostics for a multiphase oil pipeline.
Original languageEnglish
Pages (from-to)215-234
Number of pages20
JournalNeural Computation
Issue number1
Publication statusPublished - Jan 1998


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