Abstract
We propose a method to determine the relevance of the different input dimensions for a self organizing map (SOM). First, a growing self organizing map is adapted to the data. Afterwards, the effect of the input dimensions on the clustering or the topology of the SOM, respectively, is computed and the data dimensions which are ranked low are pruned. The algorithm is applied to real life satellite image data. The results are verified via visualizing the data in RGB-images as well as explicitely computing the classification error.
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References
H.-U. Bauer and K. R. Pawelzik. Quantifying the neighborhood preservation of Self-Organizing Feature Maps. IEEE Trans. on Neural Networks, 3 (4): 570–579, 1992.
H.-U. Bauer and T. Villmann. Growing a Hypercubical Output Space in a Self-Organizing Feature Map. IEEE Transactions on Neural Networks, 8 (2): 218–226, 1997.
B. Fritzke. Growing grid: a self-organizing network wirh constant neighborhood range and adaptation strength. Neural Processing Letters, 2 (5): 9–13, 1995.
P. Grassberger and I. Procaccia. Measuring the strangeness of strange attractors. Physica, 9D: 189–208, 1983.
B. Hammer and T. Villmann. Input pruning for neural gas architectures. In: M.Verleysen, editro, ESANN’2001, pages 271–276, 2001.
T. Honkela, S. Kaski, T. Kohonen, and K. Lagus. Self-organizing maps of very large document collections: Justification for the WEBSOM method. In I. Balderjahn, R. Mathar, and M. Schader, editors, Classification, Data Analysis, and Data Highways, pages 245252. Springer, Berlin, 1998.
S. Kaski. Dimensionality reduction by random mapping: fast similarity computation for clustering. In Proceedings of IJCNN’92, pages 413-. 418, 1998.
T. Kohonen. Self-Organizing Maps. Springer, 1997.
T. Martinetz and K. Schulten. Topology representing networks. Neural Networks, 7 (3): 507–522, 1993.
U. Matecki. Automatische Merkmalsauswahl fiir Neuronale Netze mit Anwendung in der pixelbezogenen Klassifikation von Bildern. Shaker, 1999.
A. Meyering and H. Ritter. Learning 3D-shape-perception with local linear maps. In Proceedings of IJCNN’92, pages 432–436, 1992.
R. Nock and M. Sebban. Sharper bounds for the hardness of prototype and feature selection. In H. Arimura, S. Jain, and A. Sharma, editors, Algorihmic Learning Theory, pages 224–237. Springer, 2000.
A. Ultsch. Self-organizing neural networks for visualization and classification. In O. Opitz, B. Lausen, and R. Klar, editors, Information and Classification, pages 307313, London, UK, 1993. Springer.
T. Villmann, R. Der, M. Herrmann, and T. Martinetz. Topology Preservation in Self-Organizing Feature Maps: Exact Definition and Measurement. IEEE Transactions on Neural Networks, 8 (2): 256–266, 1997.
T. Villmann and E. Merenyi. Extensions and modifications of SOM and its application in satellite remote sensoring processing. In H. Bothe and R.Rojas, editors, Neural Computation 2000, pages 765–771, Zürich, 2000. ICSC Academic Press.
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Bojer, T., Hammer, B., Strickert, M., Villmann, T. (2003). Determining Relevant Input Dimensions for the Self Organizing Map. In: Rutkowski, L., Kacprzyk, J. (eds) Neural Networks and Soft Computing. Advances in Soft Computing, vol 19. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1902-1_58
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DOI: https://doi.org/10.1007/978-3-7908-1902-1_58
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