GTM: the generative topographic mapping


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.

Divisions: Aston University (General)
Uncontrolled Keywords: Latent variable models,probability density,variables,linear transformations,latent space,data space,non-linear,generative topographic mapping,EM algorithm,elf-Organizing Map
ISBN: NCRG/96/015
Last Modified: 23 Feb 2024 08:05
Date Deposited: 08 Jul 2009 09:39
PURE Output Type: Technical report
Published Date: 1998-01-01
Authors: Bishop, Christopher M.
Svensén, Markus
Williams, Christopher K. I.



Version: Published Version

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