Abstract
In many real-world classifications such as video surveillance, web retrieval and image segmentation, we often encounter that class information is reflected by the pairwise constraints between data pairs rather than the usual labels for each data, which indicate whether the pairs belong to the same class or not. A common solution is combining the pairs into some new samples labeled by the constraints and then designing a smoothness-driven regularized classifier based on these samples. However, it still utilizes the limited discriminative information involved in the constraints insufficiently. In this paper, we propose a novel semi-supervised discriminatively regularized classifier (SSDRC). By introducing a new discriminative regularization term into the classifier instead of the usual smoothness-driven term, SSDRC can not only use the discriminative information more fully but also explore the local geometry of the new samples further to improve the classification performance. Experiments demonstrate the superiority of our SSDRC.
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References
Zhu, X.: Semi-supervised learning literature survey. Technical Report, 1530, Madison: Department of Computer Sciences, University of Wisconsin (2008)
Chapelle, O., Schölkopf, B., Zien, A.: Semi-Supervised Learning. MIT Press, Cambridge (2006)
Han, X., Chen, Y., Xiang, R.: Semi-supervised and Interactive Semantic Concept Learning for Scene Recognition. In: 20th International Conference on Pattern Recognition, pp. 3045–3048 (2010)
Martnez-Us, A., Sotoca, J.M.: A Semi-supervised Gaussian Mixture Model for Image Segmentation. In: 20th International Conference on Pattern Recognition, pp. 2941–2944 (2010)
Tang, W., Zhong, S.: Pairwise constraints-guided dimensinality reduction. In: SDM 2006 Workshop on Feature Selection for Data Mining, Bethesda, MD (2006)
Zhang, D., Zhou, Z., Chen, S.: Semi-supervised dimensionality reduction. In: SIAM Conference on Data Mining, ICDM (2007)
Chen, S., Zhang, D.: Semi-supervised dimensionality reduction with pairwise constraints for hyper spectral image classification. IEEE Geoscience and Remote Semsing Letters 8(2), 369–373 (2011)
Zhang, J., Yan, R.: On the value of pairwise constraints in classification and consistency. In: Proceedings of the 24th International Conference on Machine Learning, pp. 1111–1118 (2007)
Yan, R., Zhang, J., Yang, J., Hauptmann, A.: A discriminative learning framework with pairwise constraints for video object classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(4), 578–593 (2006)
Xue, H., Chen, S., Yang, Q.: Discriminatively regularized least-squares classification. Pattern Recognition 42(1), 93–104 (2009)
Yeung, D.S., Wang, D., Ng, W.W.Y., Tsang, E.C.C., Zhao, X.: Structured large margin machines: sensitive to data distributions. Machine Learning 68, 171–200 (2007)
Xue, H., Chen, S., Yang, Q.: Structural regularized support vector machine: a framework for structural large margin classifier. IEEE Trans. on Neural Networks 22(4), 573–587 (2011)
Lanckriet, G.R.G., Ghaoui, L.E., Bhattacharyya, C., Jordan, M.I.: A robust minimax approach to classfication. J. Machine Learning Research 3, 555–582 (2002)
Huang, K., Yang, H., King, I., Lyu, M.R.: Learning large margin classifiers locally and globally. In: The 21th International Conference on Machine Learning, ICML (2004)
Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: A geometric framework for learning from examples. Department of Computer Science, University of Chicago, Tech. Rep: TR-2004-06 (2004)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, New York (2001)
Pan, J.J., Pan, S.J., Yin, J., Ni, L.M., Yang, Q.: Tracking mobile users in wireless networks via semi-supervised colocalization. IEEE Trans. on Pattern Analysis and Machine Intelligence 34(3), 587–600 (2012)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Tsinghua University
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Huang, J., Xue, H., Zhai, Y. (2012). Semi-Supervised Discriminatively Regularized Classifier with Pairwise Constraints. In: Anthony, P., Ishizuka, M., Lukose, D. (eds) PRICAI 2012: Trends in Artificial Intelligence. PRICAI 2012. Lecture Notes in Computer Science(), vol 7458. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32695-0_12
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DOI: https://doi.org/10.1007/978-3-642-32695-0_12
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