The topographic neural gas

Authors:
Marian Pen̄a;Colin Fyfe
Affiliations:
University of Paisley, Paisley, Scotland;University of Paisley, Paisley, Scotland
Venue:
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Year:
2006

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Topology representing networks

Neural Networks
GTM: the generative topographic mapping

Neural Computation
Two topographic maps for data visualisation

Data Mining and Knowledge Discovery

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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.