Abstract
In this paper, we present a new approach suitable for analysis of large data sets, conditioned on the environment. Mainly, the envSOM algorithm consists of two consecutive trainings of the self-organizing map. In the first phase, a SOM is trained using every available variable, but only those which characterize the environment are used to compute the winner unit. Therefore, this phase produces an accurate model of the environment. In the second phase, a new SOM is initialized appropriately with information from the codebooks of the first SOM. The new SOM uses all the variables for winner selection. However, in this case the environmental variables are kept fixed and only the remaining ones are involved in the update process. A model of the whole data set influenced by the environmental conditions is obtained in this second phase. The result of this algorithm represents a probability function of a data set, given the environment information. Therefore, it could be very useful in the analysis of processes which have close dependencies on environmental conditions.
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References
Kangas, J., Kohonen, T., Laaksonen, J.: Variants of self-organizing maps. IEEE Transactions on Neural Networks 1, 93–99 (1990)
Kohonen, T.: Self-Organizing Maps. Springer, Heidelberg (1995)
Hagenbuchner, M., Tsoi, A.C.: A supervised training algorithm for self-organizing maps for structures. Pattern Recognition Letters 26, 1874–1884 (2005)
Melssen, W., Wehrens, R., Buydens, L.: Supervised Kohonen networks for classification problems. Chemometrics and Intelligent Laboratory Systems 83, 99–113 (2006)
Koikkalainen, P., Oja, E.: Self-organizing hierarchical feature maps. In: International Joint Conference on Neural Networks, vol. 2, pp. 279–284. IEEE, INNS (1990)
Koikkalainen, P.: Progress with the tree-structured self-organizing map. In: Cohn, A.G. (ed.) 11th European Conference on Artificial Intelligence, ECCAI (1994)
Laaksonen, J., Koskela, M., Laakso, S., Oja, E.: PicSOM - content-based image retrieval with self-organizing maps. Pattern Recognition Letters 21, 1199–1207 (2000)
Rauber, A., Merkl, D., Dittenbach, M.: The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data. IEEE Transactions on Neural Networks 13(6), 1331–1341 (2002)
Hammer, B., Micheli, A., Sperduti, A., Strickert, M.: Recursive self-organizing network models. Neural Networks 17, 1061–1085 (2004)
Guimarães, G., Sousa-Lobo, V., Moura-Pires, F.: A taxonomy of self-organizing maps for temporal sequence processing. Intelligent Data Analysis (4), 269–290 (2003)
Vesanto, J., Himberg, J., Alhoniemi, E., Parhankangas, J.: SOM toolbox for Matlab 5 (2000)
Abramowitz, G., Leuning, R., Clark, M., Pitman, A.: Evaluating the performance of land surface models. Journal of Climate 21(21), 5468–5481 (2008)
Luyssaert, S., Janssens, I.A., Sulkava, M., Papale, D., Dolman, A.J., Reichstein, M., Hollmén, J., Martin, J.G., Suni, T., Vesala, T., Loustau, D., Law, B.E., Moors, E.J.: Photosynthesis drives anomalies in net carbon-exchange of pine forests at different latitudes. Global Change Biology 13(10), 2110–2127 (2007)
Zaehle, S., Friend, A.D.: Carbon and nitrogen cycle dynamics in the o-cn land surface model: 1. model description, site-scale evaluation, and sensitivity to parameter estimates. Global Biogeochemical Cycles 24 (February 2010)
Zaehle, S., Friend, A.D., Friedlingstein, P., Dentener, F., Peylin, P., Schulz, M.: Carbon and nitrogen cycle dynamics in the o-cn land surface model: 2. role of the nitrogen cycle in the historical terrestrial carbon balance. Global Biogeochemical Cycles 24 (February 2010)
Krinner, G., Viovy, N., de Noblet-Ducoudre, N., Ogee, J., Polcher, J., Friedlingstein, P., Ciais, P., Sitch, S., Prentice, I.C.: A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Global Biogeochemical Cycles 19 (February 2005)
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Alonso, S., Sulkava, M., Prada, M.A., Domínguez, M., Hollmén, J. (2011). EnvSOM: A SOM Algorithm Conditioned on the Environment for Clustering and Visualization. In: Laaksonen, J., Honkela, T. (eds) Advances in Self-Organizing Maps. WSOM 2011. Lecture Notes in Computer Science, vol 6731. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21566-7_6
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DOI: https://doi.org/10.1007/978-3-642-21566-7_6
Publisher Name: Springer, Berlin, Heidelberg
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