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Abstract

We have recently investigated a family of algorithms which use the underlying latent space model developed for the Generative Topographic mapping(GTM) but which train the parameters in a different manner. Our first model was the Topographic Product of Experts (ToPoE) which is fast but not so data-driven as our second model, the Harmonic Topographic Mapping (HaToM). However the HaToM is much slower to train than the ToPoE. In this paper we introduce ideas from the Neural Gas algorithm to this underlying model and show that the resulting algorithm has faster convergence while retaining the good quantization properties of the HaToM.

Keywords

Data Space Vector Quantization Topology Preservation Generative Topographic Mapping Topology Preserve 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Marian Pen̄a
    • 1
  • Colin Fyfe
    • 1
  1. 1.University of PaisleyPaisleyScotland

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