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. © Springer-Verlag Berlin Heidelberg 2006.
Cite
CITATION STYLE
Pena, M., & Fyfe, C. (2006). The topographic neural gas. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4224 LNCS, pp. 241–248). Springer Verlag. https://doi.org/10.1007/11875581_29
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
