GTM: The Generative Topographic Mapping

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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. Copyright c MIT Press (1998).
Christopher M. Bishop, Markus Svensén, Chri
Added 22 Dec 2010
Updated 22 Dec 2010
Type Journal
Year 1998
Where NECO
Authors Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams
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