Abstract
How to utilize data more sufficiently is a crucial consideration in machine learning. Semi-supervised learning uses both unlabeled data and labeled data for this reason. However, Semi-Supervised Support Vector Machine (S3VM) focuses on maximizing margin only, and it abandons the instances which are not support vectors. This fact motivates us to modify maximum margin criterion to incorporate the global information contained in both support vectors and common instances. In this paper, we propose a new method, whose special variant is a semi-supervised extension of Relative Margin Machine, to utilize data more sufficiently based on S3VM and LDA. We employ Concave-Convex Procedure to solve the optimization that makes it practical for large-scale datasets, and then give an error bound to guarantee the classifier’s performance theoretically. The experimental results on several real-world datasets demonstrate the effectiveness of our method.
This work is supported in part by Natural Science Foundation of China (No. 60275025).
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. (eds.): Semi-Supervised Learning. MIT Press, Cambridge (2006)
Bennett, K.P., Demiriz, A.: Semi-supervised support vector machines. In: 12th NIPS, pp. 368–374 (1998)
Joachims, T.: Transductive inference for text classification using support vector machines. In: 16th ICML, pp. 200–209 (1999)
Huang, K., Xu, Z., King, I., Lyu, M.R.: Semi-supervised learning from general unlabeled data. In: Perner, P. (ed.) ICDM 2008. LNCS (LNAI), vol. 5077, pp. 273–282. Springer, Heidelberg (2008)
Fisher, R.A.: The use of multiple measurements in taxonomic problems. Annals Eugen. 7, 179–188 (1936)
Xiong, R., Cherkassky, V.: A combined svm and lda approach for classification. In: IJCNN, pp. 157–171 (2005)
Crammer, K., Mohri, M., Pereira, F.: Gaussian margin machines. In: 12th AISTATS (2009)
Weston, J., Collobert, R., Sinz, F., Bottou, L., Vapnik, V.: Inference with the universum. In: 23rd ICML, pp. 1009–1016 (2006)
Shivaswamy, P., Jebara, T.: Relative margin machines. In: 22nd NIPS (2008)
Chapelle, O., Sindhwani, V., Keerthi, S.S.: Optimization techniques for semi-supervised support vector machines. JMLR 9, 203–233 (2008)
Yuille, A.L., Rangarajan, A.: The concave-convex procedure (cccp). In: 15th NIPS. MIT Press, Cambridge (2001)
Collobert, R., Sinz, F., Weston, J., Bottou, L.: Large scale transductive svms. JMLR 7, 1687–1712 (2006)
Sriperumbudur, B., Lanckriet, G.: On the convergence of the concave-convex procedure. In: 23rd NIPS (2009)
El-Yaniv, R., Pechyony, D.: Transductive rademacher complexity and its applications. In: 20th COLT, pp. 157–171 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dai, B., Niu, G. (2010). Compact Margin Machine. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2010. Lecture Notes in Computer Science(), vol 6119. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13672-6_48
Download citation
DOI: https://doi.org/10.1007/978-3-642-13672-6_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13671-9
Online ISBN: 978-3-642-13672-6
eBook Packages: Computer ScienceComputer Science (R0)