Abstract
The Growing Hierarchical Self Organizing Map (GHSOM) was introduced as a dynamical neural network model that adapts its architecture during its unsupervised training process to represents the hierarchical relation of the data.
However, the dynamical algorithm of the GHSOM is sensitive to the presence of noise and outliers, and the model will no longer preserve the topology of the data space as we will show in this paper. The outliers introduce an influence to the GHSOM model during the training process by locating prototypes far from the majority of data and generating maps for few samples data. Therefore, the network will not effectively represent the topological structure of the data under study.
In this paper, we propose a variant to the GHSOM algorithm that is robust under the presence of outliers in the data by being resistant to these deviations. We call this algorithm Robust GHSOM (RGHSOM). We will illustrate our technique on synthetic and real data sets.
This work was supported in part by Research Grant Fondecyt 1040365, DGIP-UTFSM, BMBF-CHL 03-Z13 from German Ministry of Education, DIPUV-22/2004 and CID-04/2003.
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References
Alahakoon, D., Halgamuge, S., Srinivasan, B.: Dynamic self-organizing maps with controlled growth for knowledge discovery. IEEE Trans. on Neural Networks 11(3), 601–614 (2000)
Allende, H., Moraga, C., Salas, R.: Artificial neural networks in time series forescasting: A comparative analysis. Kybernetika 38(6), 685–707 (2002)
Allende, H., Moreno, S., Rogel, C., Salas, R.: Robust neural gas for the analysis of data with outliers. In: SCCC 2004, pp. 149–155. IEEE-CS Press, Los Alamitos (2004)
Allende, H., Moreno, S., Rogel, C., Salas, R.: Robust self-organizing maps. In: Sanfeliu, A., Martínez Trinidad, J.F., Carrasco Ochoa, J.A. (eds.) CIARP 2004. LNCS, vol. 3287, pp. 179–186. Springer, Heidelberg (2004)
Blake, C.L., Merz, C.J.: UCI repository of machine learning databases (1998)
University College London Neural Network Group, The elena project, http://www.dice.ucl.ac.be/neural-nets/Research/Projects/ELENA/elena.htm
Hampel, F.R., Ronchetti, E.M., Rousseeuw, P.J., Stahel, W.A.: Robust statistics. Wiley Series in Probability and Mathematical Statistics (1986)
Huber, P.J.: Robust statistics. Wiley Series in probability and mathematical statistics (1981)
Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer Series in Information Sciences, vol. 30. Springer, Heidelberg (2001)
Mangasarian, O., Street, W., Wolberg, W.: Breast cancer diagnosis and prognosis via linear programming. Operations Research 43(4), 570–577 (1995)
Prechelt, L.: Proben1 - a set of benchmarks and benchmarking rules for neural training algorithms, Technical Report 21/94, Fakultat fur Informatik, Universitat Karlsruhe, Germany (1994)
Rauber, A., Merkl, D., Dittenbach, M.: The growing hierarchical self-organizing map: Exploratory analysis of high-dimensional data. IEEE Trans. on Neural Networks 13(6), 1331–1341 (2002)
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Moreno, S., Allende, H., Rogel, C., Salas, R. (2005). Robust Growing Hierarchical Self Organizing Map. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_42
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DOI: https://doi.org/10.1007/11494669_42
Publisher Name: Springer, Berlin, Heidelberg
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