Abstract
In a number of real-life applications, the user is interested in analyzing non vectorial data, for which kernels are useful tools that embed data into an (implicit) Euclidean space. However, when using such approaches with prototype-based methods, the computational time is related to the number of observations (because the prototypes are expressed as convex combinations of the original data). Also, a side effect of the method is that the interpretability of the prototypes is lost. In the present paper, we propose to overcome these two issues by using a bagging approach. The results are illustrated on simulated data sets and compared to alternatives found in the literature.
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
Mac Donald, D., Fyfe, C.: The kernel self organising map. In: Proceedings of 4th International Conference on Knowledge-Based Intelligence Engineering Systems and Applied Technologies, pp. 317–320 (2000)
Lau, K., Yin, H., Hubbard, S.: Kernel self-organising maps for classification. Neurocomputing 69, 2033–2040 (2006)
Boulet, R., Jouve, B., Rossi, F., Villa, N.: Batch kernel SOM and related laplacian methods for social network analysis. Neurocomputing 71(7-9), 1257–1273 (2008)
Hammer, B., Hasenfuss, A.: Topographic mapping of large dissimilarity data sets. Neural Computation 22(9), 2229–2284 (2010)
Olteanu, M., Villa-Vialaneix, N.: On-line relational and multiple relational SOM. Neurocomputing (2013) (forthcoming)
Olteanu, M., Villa-Vialaneix, N., Cierco-Ayrolles, C.: Multiple kernel self-organizing maps. In: Verleysen, M. (ed.) XXIst European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Bruges, Belgium, pp. 83–88. i6doc.com (2013)
Massoni, S., Olteanu, M., Villa-Vialaneix, N.: Which distance use when extracting typologies in sequence analysis? An application to school to work transitions. In: International Work Conference on Artificial Neural Networks (IWANN 2013), Puerto de la Cruz, Tenerife (2013)
Hofmann, D., Hammer, B.: Sparse approximations for kernel learning vector quantization. In: Verleysen, M. (ed.) Proceedings of XXIst European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Bruges, Belgium, pp. 549–554. i6doc.com (2013)
Aronszajn, N.: Theory of reproducing kernels. Transactions of the American Mathematical Society 68(3), 337–404 (1950)
Smola, A.J., Kondor, R.: Kernels and regularization on graphs. In: Schölkopf, B., Warmuth, M.K. (eds.) COLT/Kernel 2003. LNCS (LNAI), vol. 2777, pp. 144–158. Springer, Heidelberg (2003)
Petrakieva, L., Fyfe, C.: Bagging and bumping self organising maps. Computing and Information Systems Journal 9, 69–77 (2003)
Vrusias, B., Vomvoridis, L., Gillam, L.: Distributing SOM ensemble training using grid middleware. In: Proceedings of IEEE International Joint Conference on Neural Networks (IJCNN 2007), pp. 2712–2717 (2007)
Baruque, B., Corchado, E.: Fusion methods for unsupervised learning ensembles. SCI, vol. 322. Springer, Heidelberg (2011)
Villa, N., Rossi, F.: A comparison between dissimilarity SOM and kernel SOM for clustering the vertices of a graph. In: 6th International Workshop on Self-Organizing Maps (WSOM), Bielefield, Germany, Neuroinformatics Group, Bielefield University (2007)
Polzlbauer, G.: Survey and comparison of quality measures for self-organizing maps. In: Paralic, J., Polzlbauer, G., Rauber, A. (eds.) Proceedings of the Fifth Workshop on Data Analysis (WDA 2004), Sliezsky dom, Vysoke Tatry, Slovakia, pp. 67–82. Elfa Academic Press (2004)
McAuley, J., Leskovec, J.: Learning to discover social circles in ego networks. In: NIPS Workshop on Social Network and Social Media Analysis (2012)
Rossi, F., Villa-Vialaneix, N.: Optimizing an organized modularity measure for topographic graph clustering: a deterministic annealing approach. Neurocomputing 73(7-9), 1142–1163 (2010)
Fouss, F., Pirotte, A., Renders, J., Saerens, M.: Random-walk computation of similarities between nodes of a graph, with application to collaborative recommendation. IEEE Transactions on Knowledge and Data Engineering 19(3), 355–369 (2007)
von Luxburg, U.: A tutorial on spectral clustering. Statistics and Computing 17(4), 395–416 (2007)
Danon, L., Diaz-Guilera, A., Duch, J., Arenas, A.: Comparing community structure identification. Journal of Statistical Mechanics, P09008 (2005)
Newman, M., Girvan, M.: Finding and evaluating community structure in networks. Physical Review, E 69, 026113 (2004)
Fruchterman, T., Reingold, B.: Graph drawing by force-directed placement. Software, Practice and Experience 21, 1129–1164 (1991)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Mariette, J., Olteanu, M., Boelaert, J., Villa-Vialaneix, N. (2014). Bagged Kernel SOM. In: Villmann, T., Schleif, FM., Kaden, M., Lange, M. (eds) Advances in Self-Organizing Maps and Learning Vector Quantization. Advances in Intelligent Systems and Computing, vol 295. Springer, Cham. https://doi.org/10.1007/978-3-319-07695-9_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-07695-9_4
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07694-2
Online ISBN: 978-3-319-07695-9
eBook Packages: EngineeringEngineering (R0)