GTM: the generative topographic mapping

Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams

Research output: Preprint or Working paperTechnical report

Abstract

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 transformations between the latent space and the data space. In this paper we introduce a form of non-linear latent variable model called the Generative Topographic Mapping, for which the parameters of the model can be determined using the EM 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 multi-phase oil pipeline.
Original languageEnglish
Place of PublicationBirmingham
PublisherAston University
Number of pages16
ISBN (Print)NCRG/96/015
Publication statusPublished - 1 Jan 1998

Keywords

  • Latent variable models
  • probability density
  • variables
  • linear transformations
  • latent space
  • data space
  • non-linear
  • generative topographic mapping
  • EM algorithm
  • elf-Organizing Map

Fingerprint

Dive into the research topics of 'GTM: the generative topographic mapping'. Together they form a unique fingerprint.
  • GTM: the generative topographic mapping

    Bishop, C. M., Svensén, M. & Williams, C. K. I., 1 Jan 1998, In: Neural Computation. 10, 1, p. 215-235 21 p.

    Research output: Contribution to journalArticlepeer-review

Cite this