GTM paper and software available

[Prof. Chris Bishop]

               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.

To access other publications by the Neural Computing Research Group, go
to the group home page:

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

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