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               GTM: The Generative Topographic Mapping
               =======================================

  Christopher M. Bishop, Markus Svensen and Christopher K. I. Williams

           Accepted for publication in Neural Computation

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.

Available as a postscript file from the GTM home page:

  http://www.ncrg.aston.ac.uk/GTM/

This home page also provides a Matlab implementation of GTM as well as 
data sets used in its development. Related technical reports are 
available here too.

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