Abstract
Most graph-based semi-supervised learning methods model the structure of a dataset as a single k-NN graph. Although graph construction is an important task, many existing graph-based methods build a graph from a dataset directly and naively. While the resulting k-NN graph provides relatively a good representation of the dataset,it generally produces inappropriate shortcuts on cluster boundaries. In this paper, we propose a novel approach for modeling and combining multiple graphs with different edge weights to avoid such undesirable behavior. Using the combination of those graphs, we can systematically reduce the effect of noise in conceptually similar fashion to an ensemble approach. Experimental results demonstrate that our approach improves classification accuracy on both benchmark and artificial 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
Chapelle, O., Schölkopf, B., Zien, A.: Semi-Supervised Learning. MIT Press, Cambridge (2006)
Zhu, X., Ghahamani, Z.: Semi-supervised learning using gaussian fields and harmonic functions. In: Proc. of the 20th ICML, pp. 912–919 (2003)
Joachims, T.: Transductive learning via spectral graph partitioning. In: Proc. of the 20th ICML, pp. 290–297 (2003)
Zhu, X.: Semi-supervised learning literature survey (2005), http://www.cs.wisc.edu/~jerryzhu/pub/ssl_survey.pdf
Fagin, R., Karlin, A.R., Kleinberg, J., Raghavan, P., Rajagopalan, S., Rubinfeld, R., Sudan, M., Tomkins, A.: Random walks with back buttons. Annals of Applied Probability 11(3), 810–862 (2001)
Diestel, R.: Graph Theory. Springer, New York (2004)
Joachims, T.: Transductive inference for text classification using support vector machines. In: Proc. of the 16th ICML, pp. 200–209 (1999)
Alpaydin, E.: Introduction to Machine Learning. MIT Press, Cambridge (2004)
GarcÃa-Pedrajas, N., GarcÃa-Osorio, C., Fyfe, C.: Nonlinear boosting projections for ensemble construction. J. Mach. Learn. Res. 8, 1–33 (2007)
Wang, F., Zhang, C.: Label popagation through linear neighborhoods. In: Proc. of the 23rd ICML, pp. 985–992 (2006)
Zhou, X., Li, C.: Combining smooth graphs with semi-supervised classification. In: Ng, W.-K., Kitsuregawa, M., Li, J., Chang, K. (eds.) PAKDD 2006. LNCS (LNAI), vol. 3918, pp. 400–409. Springer, Heidelberg (2006)
Shin, H., Hill, N.J., Rätsch, G.: Graph based semi-supervised learning with sharper edges. In: Proc. of 17th ECML, pp. 401–412 (2006)
Moss, F., Ward, L.M., Sannita, W.G.: Stochastic resonance and sensory information processing: a tutorial and review of application. Clinical Neurophysiology 115, 267–281 (2004)
Nobori, T., Matsui, N.: Stochastic resonance neural network and its performance. In: Proc. of IJCNN 2000, vol. 2, p. 2013 (2000)
Nagao, T.: Random Matrices: An Introduction. University of Tokyo Press (2005)
Achlioptas, D.: Random matrices in data analysis. In: Proc. of the 8th PKDD, pp. 1–7 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer Berlin Heidelberg
About this paper
Cite this paper
Izutani, A., Uehara, K. (2008). A Modeling Approach Using Multiple Graphs for Semi-Supervised Learning. In: Jean-Fran, JF., Berthold, M.R., Horváth, T. (eds) Discovery Science. DS 2008. Lecture Notes in Computer Science(), vol 5255. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88411-8_28
Download citation
DOI: https://doi.org/10.1007/978-3-540-88411-8_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88410-1
Online ISBN: 978-3-540-88411-8
eBook Packages: Computer ScienceComputer Science (R0)