Abstract
In this paper, we consider the problem of combining the labeled and unlabeled examples to boost the performance of semi-supervised learning. We first define the label information graph, and then incorporate it with neighborhood graph. We propose a new regularized semi-supervised classification algorithm, in which the regularization term is based on this modified Graph Laplacian. According to the properties of Reproducing Kernel Hilbert Space (RKHS), the representer theorem holds, so the solution can be expressed by the Mercer kernel of examples. Experimental results show that our algorithm can use unlabeled and labeled examples effectively.
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
Belkin, M., Niyogi, P., Sindhwani, V.: Manifold Regularization: A Geometric Framework for Learning from Examples. Department of Computer Science, University of Chicago, TR-2004-06
Belkin, M., Niyogi, P., Sindhwani, V.: On Manifold Regularization. Department of Computer Science. University of Chicago, TR-2004-05
Belkin, M., Niyogi, P.: Laplacian Eigenmaps for Dimensionality Reduction and Data Representation. Neural Computation 15(6), 1373–1396 (2003)
Blum, A., Mitchell, T.: Combining Labeled and Unlabeled Data with Co-training. In: Proceedings of the Eleventh Annual Conference on Computational Learning Theory, Madison, Wisconsin, USA, pp. 92–100. ACM, New York (1998)
Szummer, M., Jaakkola, T.: Partially Labeled Classification with Markov Random Walks. In: Dietterich, T.G., Becker, S., Ghahramani, Z. (eds.) Advances in Neural Information Processing Systems, vol. 14, pp. 945–952. MIT Press, Cambridge (2001)
Zhu, X., Ghahramani, Z., Lafferty, J.: Semi-supervised Learning Using Gaussian Fields and Harmonic Functions. In: Fawcett, T., Mishra., N. (eds.) Proceedings of the Twentieth International Conference on Machine Learning, pp. 912–919. AAAI Press, Menlo Park (2003)
Blum, A., Chawla, S.: Learning from Labeled and Unlabeled Data Using Graph Mincuts. In: Brodley, C.E., Danyluk, A.P. (eds.) Proceedings of the Eighteenth International Conference on Machine Learning, USA, pp. 19–26. Morgan Kaufmann, San Francisco (2001)
Zhou, D., Bousquet, O., Lal, T.N., Weston, J., Schoelkopf, B.: Learning with Local and Global Consistency. In: Thrun, S., Sauland, L., Sch"olkopf, B. (eds.) Advances in Neural Information Processing Systems, vol. 16. MIT Press, Cambridge (2004)
Bousquet, O., Chapelle, O., Hein, M.: Measure Based Regularization. In: Thrun, S., Sauland, L., Scholkopf, B. (eds.) Advances in Neural Information Processing Systems. MIT Press, Cambridge (2004)
Szummer, M., Jaakkola, T.: Information Regularization with Partially Labeled Data. In: Becker, S., Thrun, S., Obermayer, K. (eds.) Advances in Neural Information Processing Systems, Canada, vol. 15, pp. 1025–1032. MIT Press, Cambridge (2003)
Krishnapuram, B., Williams, D., Ya, X., Hartemink, A., Carin, L., Carin, L., Figueiredo, M.A.T.: On Semi-Supervised Classification. In: Saul, L.K., Weiss, Y., Bottou, L. (eds.) Advances in Neural Information Processing Systems, vol. 17, pp. 721–728. MIT Press, Cambridge (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhao, L., Luo, S., Tian, M., Shao, C., Ma, H. (2006). Combining Label Information and Neighborhood Graph for Semi-supervised Learning. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_72
Download citation
DOI: https://doi.org/10.1007/11759966_72
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34439-1
Online ISBN: 978-3-540-34440-7
eBook Packages: Computer ScienceComputer Science (R0)