Developments of the generative topographic mapping

13 years 2 months ago
Developments of the generative topographic mapping
The Generative Topographic Mapping (GTM) model was introduced by 7) as a probabilistic re-formulation of the self-organizing map (SOM). It offers a number of advantages compared with the standard SOM, and has already been used in a variety of applications. In this paper we report on several extensions of the GTM, including an incremental version of the EM algorithm for estimating the model parameters, the use of local subspace models, extensions to mixed discrete and continuous data, semi-linear models which permit the use of high-dimensional manifolds whilst avoiding computational intractability, Bayesian inference applied to hyper-parameters, and an alternative framework for the GTM based on Gaussian processes. All of these developments directly exploit the probabilistic structure of the GTM, thereby allowing the underlying modelling assumptions to be made explicit. They also highlight the advantages of adopting a consistent probabilistic framework for the formulation of pattern re...
Christopher M. Bishop, Markus Svensén, Chri
Added 22 Dec 2010
Updated 22 Dec 2010
Type Journal
Year 1998
Where IJON
Authors Christopher M. Bishop, Markus Svensén, Christopher K. I. Williams
Comments (0)