Summary
In this paper we propose a strategy, the Tree-based Topology-Oriented SOM (TTO-SOM) by which we can impose an arbitrary, user-defined, tree-like topology onto the codebooks. Such an imposition enforces a neighborhood phenomenon which is based on the user-defined tree, and consequently renders the so-called bubble of activity to be drastically different from the ones defined in the prior literature. The map learnt as a consequence of training with the TTO-SOM is able to infer both the distribution of the data and its structured topology interpreted via the perspective of the user-defined tree. The TTO-SOM also reveals multi-resolution capabilities, which are helpful for representing the original data set with different numbers of points, whithout the necessity of recomputing the whole tree. The ability to extract an skeleton, which is a “stick-like” representation of the image in a lower dimensional space, is discussed as well. These properties have been confirmed by our experimental results on a variety of data sets.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Astudillo, C.A., Oommen, B.J.: Unabridged version of this paper (2008)
Datta, A., Parui, S.M., Chaudhuri, B.B.: Skeletal shape extraction from dot patterns by self-organization. Pattern Recognition 4, 80–84 (1996)
Dittenbach, M., Merkl, D., Rauber, A.: The Growing Hierarchical Self-Organizing Map. In: Proc of the International Joint Conference on Neural Networks (IJCNN 2000), Como, Italy, pp. 15–19 (2000)
Fritzke, B.: A growing neural gas network learns topologies. In: Advances in Neural Information Processing Systems, vol. 7, pp. 625–632. MIT Press, Cambridge (1995)
Fritzke, B.: Growing Grid - a self-organizing network with constant neighborhood range and adaptation strength. Neural Processing Letters 2, 9–13 (1995)
Guan, L.: Self-Organizing Trees and Forests: A Powerful Tool in Pattern Clustering and Recognition. In: Campilho, A., Kamel, M.S. (eds.) ICIAR 2006. LNCS, vol. 4141, pp. 1–14. Springer, Heidelberg (2006)
Kohonen, T.: Self-Organizing Maps. Springer, New York (2001)
Koikkalainen, P., Oja, E.: Self-organizing hierarchical feature maps. In: IJCNN International Joint Conference on Neural Networks, vol. 2, pp. 279–284 (1990)
Merkl, D., He, S., Dittenbach, M., Rauber, A.: Adaptive hierarchical incremental grid growing: An architecture for high-dimensional data visualization. In: Proceedings of the 4th Workshop on Self-Organizing Maps, Advances in Self-Organizing Maps, Kitakyushu, Japan, pp. 293–298 (2003)
Ogniewicz, O.L., Kübler, O.: Hierarchic Voronoi Skeletons. Pattern Recognition 28, 343–359 (1995)
Pakkanen, J.: The Evolving Tree, a new kind of self-organizing neural network. In: Proceedings of the Workshop on Self-Organizing Maps 2003, Kitakyushu, Japan, pp. 311–316 (2003)
Pakkanen, J., Iivarinen, J., Oja, E.: The Evolving Tree — A Novel Self-Organizing Network for Data Analysis. Neural Processing Letters 20, 199–211 (2004)
Rauber, A., Merkl, D., Dittenbach, M.: The Growing Hierarchical Self-Organizing Map: exploratory analysis of high-dimensional data. IEEE Transactions on Neural Networks 13, 1331–1341 (2002)
Singh, R., Cherkassky, V., Papanikolopoulos, N.: Self-Organizing Maps for the skeletonization of sparse shapes. IEEE Transactions on Neural Networks 11, 241–248 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Astudillo, C.A., Oommen, J.B. (2009). A Novel Self Organizing Map Which Utilizes Imposed Tree-Based Topologies. In: Kurzynski, M., Wozniak, M. (eds) Computer Recognition Systems 3. Advances in Intelligent and Soft Computing, vol 57. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-93905-4_21
Download citation
DOI: https://doi.org/10.1007/978-3-540-93905-4_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-93904-7
Online ISBN: 978-3-540-93905-4
eBook Packages: EngineeringEngineering (R0)