Abstract
Spatialization methods create visualizations that allow users to analyze high-dimensional data in an intuitive manner and facilitates the extraction of meaningful information. Just as geographic maps are simplified representations of geographic spaces, these visualizations are essentially maps of abstract data spaces that are created through dimensionality reduction.
Recently, we proposed to use the spherical Geodesic SOM for creating an information landscape that represents an abstract data space and can capture a manifold’s global structure. Path finding was then applied to approximate the geodesic path in the feature space and some promising preliminary results were obtained. Based on these results, we propose a novel approach for measuring the preservation of geodesics by analyzing the paths on the information landscapes. The effectiveness of the measure is then evaluated through various data sets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Chen, C.: Information visualization. Information Visualization 1(1), 1–4 (2002)
Tory, M., Sprague, D., Wu, F., So, W.Y., Munzner, T.: Spatialization design: Comparing points and landscapes. IEEE Transactions on Visualization and Computer Graphics 13(6), 1262–1269 (2007)
Spence, R.: Information Visualization. Addison Wesley, Reading (2000)
Montello, D.R., Fabrikant, S.I., Ruocco, M., Middleton, R.S.: Testing the first law of cognitive geography on point-display spatializations. In: Kuhn, W., Worboys, M.F., Timpf, S. (eds.) COSIT 2003. LNCS, vol. 2825, pp. 316–331. Springer, Heidelberg (2003)
Ultsch, A., Siemon, H.P.: Exploratory data analysis: Using kohonen networks on transputers. Technical Report 329, University of Dortmund (1989)
Bauer, H.U., Pawelzik, K.: Quantifying the neighborhood preservation of self-organizing feature maps. IEEE Transactions on Neural Networks 3(4), 570–579 (1992)
Villmann, T., Der, R., Herrmann, M., Martinetz, T.M.: Topology preservation in self-organizing feature maps: exact definition and measurement. IEEE Transactions on Neural Networks 8(2), 256–266 (1997), doi:10.1109/72.557663
Revuelta, F.F., Chamizo, J.M.G., Rodríguez, J.G., Sáez, A.H.: Geodesic topographic product: An improvement to measure topology preservation of self-organizing neural networks. In: Lemaître, C., Reyes, C.A., González, J.A. (eds.) IBERAMIA 2004. LNCS (LNAI), vol. 3315, pp. 841–850. Springer, Heidelberg (2004)
Doherty, K., Adams, R., Davey, N.: Topological correlation. In: ESANN 2006: Proceedings of the 14th European Symposium on Artificial Neural Networks, April 2006, pp. 125–130 (2006)
Takatsuka, M.: An application of the self-organizing map and interactive 3-d visualization to geospatial data. In: Proceedings of the 6th International Conference on GeoComputation (September 2001)
Bui, M., Takatsuka, M.: Path finding on a spherical SOM using the distance transform and floodplain analysis. In: Proceedings of the Workshop on Self-Organizing Maps, WSOM 2007 (2007)
Asuncion, A., Newman, D.: UCI machine learning repository (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bui, M., Takatsuka, M. (2009). Quantifying the Path Preservation of SOM-Based Information Landscapes. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5864. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10684-2_91
Download citation
DOI: https://doi.org/10.1007/978-3-642-10684-2_91
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10682-8
Online ISBN: 978-3-642-10684-2
eBook Packages: Computer ScienceComputer Science (R0)