Abstract
Self-Organizing Map (SOM), being a prominent unsupervised learning algorithm, is often used for multivariate data visualization. However, SOM only preserves inter-neurons distances in the input space and not in the output space due to the rigid grid used in SOM. Visualization-induced Self-Organizing Map (ViSOM) has been proposed as a visualization-wise improved variation of the popular unsupervised SOM. However ViSOM suffers from dead neuron problem as a huge number of neurons fall outside of the data region due to the regularization effect, even when the regularization control parameter is properly chosen. In this paper, a hybrid ViSOM that employs a modified Adaptive Coordinates (AC) technique is proposed for data visualization. Empirical studies of the hybrid technique yield promising topology preserved visualizations and data structure exploration for synthetic as well as benchmarking datasets.
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.
References
Sammon, J.W.: A nonlinear mapping for data structure analysis. IEEE Trans. on Computer C-18, 401–409 (1969)
Shepard, R.A., Carrol, J.D.: Parametric representation of nonlinear data structures. In: Krishnaiah, P.R. (ed.) Proc. of. Int’l. Symp. Multivariate Analysis, pp. 561–592. Academic, New York (1965)
Kohonen, T.: Self-organizing maps, 2nd edn. Springer, New York (1997)
Yin, H.: ViSOM: A novel method for multivariate data projection and structure visualization. IEEE Trans. on Neural Network 13(1), 237–243 (2002)
Wu, S.T., Chow, S.W.: PRSOM: A new visualization method by hybriding multidimensional scaling and self-organizing map. IEEE Trans. on Neural Network 16(6), 1362–1380 (2005)
Merkl, D., Rauber, A.: Alternative ways for cluster visualization in self-organizing maps. In: Proc. Workshop on SOM, Espoo, Finland, pp. 106–111 (1997)
Merkl, D., Rauber, A.: On the similarity of eagles, hawks, and cows: visualization of semantic similarity in self-organizing maps. In: Proc. Int’l Workshop on Fuzzy-Neuro-Systems 1997 (FNS 1997), Soest, Germany (1997)
Teh, C.S., Sarwar, Z.T.: A Supervised ANN for classification and data visualization. In: Proc. of IEEE World Congress on Computational Intelligence, WCCI 2008 (International Joint Conference on Neural Networks (IJCNN 2008)), pp. 556–563 (2008)
Teh, C.S., Yii, M.L., Chen, C.J.: An artificial neural network model for multi dimensional reduction and data structure exploration. In: Int’l Conf. of Soft Computing and Pattern Reconigtion (SoCPaR 2009), pp. 254–258 (2009)
Newman, D.J., Hettich, S., Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases, Dept. of ICS, Univ. of California at Irvine (1998)
Oprea, A.E., Strungaru, R., Ungureanu, G.M.: A self organizing map approach to breast cancer detection. In: Int’l Conf. of IEEE in Engineering in Medicine and Biology Society, Canada, pp. 3032–3035 (2008)
Wu, S., Chow, T.W.S.: Self-organizing and self-evolving neurons: a new neural network for optimization. IEEE Trans. on Neural Networks 18(2), 385–394 (2007)
Tasdemir, K.: Graph based representations of density distribution and distances for self-organizing maps. IEEE Trans. on Neural Networks 21(3), 520–526 (2010)
Ding, M., Tellez, R.D., Hu, K.: Mapping vulnerability to debris flows based on SOM method. In: IEEE Int’l. Conf. on Computuer and Automation Engineering, pp. 393–398 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Teh, C.S., Yii, M.L., Chen, C.J., Sarwar, Z.T. (2012). A Hybrid Visualization-Induced Self-Organizing Map for Multi Dimensional Reduction and Data Visualization. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7664. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34481-7_34
Download citation
DOI: https://doi.org/10.1007/978-3-642-34481-7_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34480-0
Online ISBN: 978-3-642-34481-7
eBook Packages: Computer ScienceComputer Science (R0)