Abstract
Growing Neural Gas is a self organizing network capable to build a lattice of neural unit that grows in the input pattern manifold. The structure of the obtained network often is not a planar graph and can be not suitable for visualization; cluster identification is possible only if a set of not connected subgraphs are produced. In this work we propose a method to select the neural units in order to extract the information on the pattern clusters, even if the obtained network graph is connected. The proposed method creates a new structure called Labeling Network (LNet) that repeats the topology of the GNG network and a set of weights to the links of the neuron graph. These weights are trained using an anti-Hebbian algorithm obtaining a new structure capable to label input patterns according to their cluster.
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References
Kohonen, T.: Self Organizing Maps. Springer, Heidelberg (1997)
Martinetz, T.M., Berkovich, S.G., Schulten, K.J.: Neural Gas Network for Vector Quantization and its Application to Time-Series Prediction. IEEE Trans. on Neural Networks 4(4), 558–569 (1993)
Fritzke, B.: A growing neural gas network learns topologies. In: Tesauro, G., Touretzky, D.S., Leen, T.K. (eds.) Advances in Neural Information Processing Systems, pp. 625–632. MIT Press, Cambridge (1995)
Fu, L.: A Concept Learning Network Based on Correlation and Backpropagation. IEEE Trans. on System, Man and Cybernetics part. B 29(6), 912–916 (1999)
Barreto, G.A., Arajo, A.F.R.: The role of excitatory and inhibitory learning in EXIN networks. In: Proceedings of the IEEE World Congress on Computational Intelligence, pp. 2378–2383. IEEE Press, Los Alamitos (1998)
Marshall, J.A.: Adaptive perceptual pattern recognition by self-organizing neural networks: Context, uncertainty, multiplicity and scale. Neural Networks 8, 335–362 (1995)
Heskes, T.: Energy functions for self-organizing maps. In: Oja, E., Kaski, S. (eds.) Kohonen Maps, pp. 303–316. Elsevier, Amsterdam (1999)
Costa, J.A.F., Oliveira, R.S.: Cluster Analysis using Growing Neural Gas and Graph Partitioning. In: Proceedings of International Joint Conference on Neural Networks, Orlando, Florida, USA (2007)
Qin, A.K., Suganthan, P.N.: Robust growing neural gas algorithm with application in cluster analysis. Neural networks 17(8-9), 1135–1148 (2004)
Iris Dataset in Orange website, http://www.ailab.si/orange/doc/datasets/iris.htm
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Rizzo, R., Urso, A. (2009). Identifying Clusters Using Growing Neural Gas: First Results. In: Alippi, C., Polycarpou, M., Panayiotou, C., Ellinas, G. (eds) Artificial Neural Networks – ICANN 2009. ICANN 2009. Lecture Notes in Computer Science, vol 5768. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04274-4_56
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DOI: https://doi.org/10.1007/978-3-642-04274-4_56
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