Abstract
Vector quantization of large datasets can be carried out by means of an incremental modelling approach where the modelling task is transformed into an incremental task by partitioning or sampling the data, and the resulting datasets are processed by means of an incremental learner. Growing Neural Gas is an incremental vector quantization algorithm with the capabilities of topology-preserving and distribution-matching. Distribution matching can produce overpopulation of prototypes in zones with high density of data. In order to tackle this drawback, we introduce some modifications to the original Growing Neural Gas algorithm by adding three new parameters, one of them controlling the distribution of the codebook and the other two controlling the quantization error and the amount of units in the network. The resulting learning algorithm is capable of efficiently quantizing large datasets presenting high and low density regions while solving the prototype proliferation problem.
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References
Alpaydin, E.: Introduction to Machine Learning. MIT Press, Cambridge (2004)
Bouchachia, A., Gabrys, B., Sahel, Z.: Overview of Some Incremental Learning Algorithms. In: Fuzzy Systems Conference. FUZZ-IEEE 2007, vol. 23-26, pp. 1–6 (2007)
Bradley, P., Gehrke, J., Ramakrishnan, R., Srikant, R.: Scaling mining algorithms to large databases. Commun. ACM 45, 38–43 (2002)
Cselenyi, Z.: Mapping the dimensionality, density and topology of data: The growing adaptive neural gas. Computer Methods and Programs in Biomedicine 78, 141–156 (2005)
Fritzke, B.: Unsupervised ontogenic networks. In: Handbook of Neural Computation, ch. C 2.4, Institute of Physics, Oxford University Press (1997)
Fritzke, B.: Goals of Competitive Learning. In: Some Competitive Learning Methods (1997), http://www.neuroinformatik.rub.de/VDM/research/gsn/JavaPaper/ (Cited October 26, 2008)
Fritzke, B.: A Growing Neural Gas Learns Topologies. In: Advances in Neural Information Processing Systems, vol. 7. MIT Press, Cambridge (1995)
Ganti, V., Gehrke, J., Ramakrishnan, R.: Mining very large databases. Computer 32, 38–45 (1999)
Giraud-Carrier, C.: A note on the utility of incremental learning. AI Commun. 13, 215–223 (2000)
Heinke, D., Hamker, F.H.: Comparing neural networks: a benchmark on growing neural gas, growing cell structures, and fuzzy ARTMAP. IEEE Transactions on Neural Networks 9, 1279–1291 (1998)
Hijmans, R., Cameron, S., Parra, J., Jones, P., Jarvis, A.: Very High Resolution Interpolated Climate Surfaces for Global Land Areas. Int. J. Climatol 25, 1965–1978 (2005)
Martinetz, T., Schulten, K.: Topology representing networks. Neural Networks 7, 507–522 (1994)
Martinetz, T., Schulten, K.: A neural gas network learns topologies. Artificial Neural Networks, 397–402 (1991)
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Satizábal, H.F., Pérez-Uribe, A., Tomassini, M. (2009). Avoiding Prototype Proliferation in Incremental Vector Quantization of Large Heterogeneous Datasets. In: Franco, L., Elizondo, D.A., Jerez, J.M. (eds) Constructive Neural Networks. Studies in Computational Intelligence, vol 258. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04512-7_13
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DOI: https://doi.org/10.1007/978-3-642-04512-7_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04511-0
Online ISBN: 978-3-642-04512-7
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