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.
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References
Bishop, C.M., Svensen, M., Williams, C.K.I.: GTM: The Generative Topographic Mapping. Neural Computation (1997)
Cottrell, M., Hammer, B., Hasenfu, A., Villmann, T.: Batch neural gas. In: WSOM (2005)
Fritzke, F.A.: Growing Neural Gas Network Learns Topologies. In: Advances in Neural Information Processing Systems 7 (NIPS 1994), pp. 625–632. MIT Press, Cambridge (1995)
Fyfe, C.: Two topographic maps for data visualization. Data Mining and Knowledge Discovery (2006)
Kohonen, T.: Self-Organization and Associative Memory. Springer, Heidelberg (1984)
Martinetz, T.M., Berkovich, S.G., Schulten, K.J.: ’Neural-gas’ network for vector quantization and its application to time-series prediction. IEEE Transactions on Neural Networks 4(4), 558–569 (1993)
Martinetz, T., Schulten, K.: Topology representing networks. Neural Networks 7, 507–522 (1994)
Neural Networks Research Centre, Helsinki University of Technology, SOM Toolbox, http://www.cis.hut.fi/projects/somtoolbox/
Peña, M., Fyfe, C.: Model- and Data-driven Harmonic Topographic Maps. WSEAS Transactions on Computers 4(9), 1033–1044 (2005)
Peña, M., Fyfe, C.: Outlier Identification with the Harmonic Topographic Mapping. In: 14 th European Symposium on Artificial Neural Networks, ESANN (2006)
Zhang, B.: Generalized K-Harmonic Means – Boosting in Unsupervised Learning. Tech. report. HP Laboratories, Palo Alto (2000)
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Pen̄a, M., Fyfe, C. (2006). The Topographic Neural Gas. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_29
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DOI: https://doi.org/10.1007/11875581_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45485-4
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