Abstract
Semi-supervised learning has received considerable attention in the machine learning literature due to its potential in reducing the need for expensive labeled data. The majority of the proposed algorithms, however, have been applied to the classification task. In this paper we present a graph-based semi-supervised algorithm for solving regression problem. Our method incorporates an adjacent graph, which is built on labeled and unlabeled data, with the standard Gaussian process (GP) prior to infer the new training and predicting distribution for semi-supervised GP regression (GPr). Additionally, in semi-supervised regression, the prediction of unlabeled data could contain some valuable information. For example, it can be seen as labeled data paired with the unlabeled data, and under some metrics, they can help to construct more accurate model. Therefore, we also describe a feedback algorithm, which can choose the useful prediction of unlabeled data for feedback to re-train the model iteratively. Experimental results show that our work achieves comparable performance to standard GPr.
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References
Belkin, M., Niyogi, P., Sindhwani, V.: Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples. The Journal of Machine Learning Research 7, 2399–2434 (2006)
Blum, A., Mitchell, T.: Combining Labeled and Unlabeled Data with Co-training. In: Proc. of the 11th Annual Conference on Computational Learning Theory, pp. 92–100 (1998)
Brefeld, U., Gärtner, T., Scheffer, T., Wrobel, S.: Efficient Co-regularized Least Squares Regression. In: Proc. of the 23rd International Conference on Machine Learning, pp. 137–144 (2006)
Brouard, C., d’Alché-Buc, F., Szafranski, M.: Semi-supervised Penalized Output Kernel Regression for Link Prediction. In: Proc. of the 28th International Conference on Machine Learning, pp. 593–600 (2011)
Kim, K.I., Steinke, F., Hein, M.: Semi-supervised Regression using Hessian Energy with an Application to Semi-supervised Dimensionality Reduction. In: Advances in Neural Information Processing Systems, vol. 22, pp. 979–987 (2009)
Lin, B.B., Zhang, C.Y., He, X.F.: Semi-supervised Regression via Parallel Field Regularization. In: Advances in Neural Information Processing Systems, vol. 24, pp. 433–441 (2011)
Liu, Y., Liu, Y., Zhong, S., Chan, K.C.C.: Semi-supervised Manifold Ordinal Regression for Image Ranking. In: Proc. of the 19th ACM International Conference on Multimedia, pp. 1393–1396 (2011)
Nigam, K., McCallum, A.K., Thrun, S., Mitchell, T.: Text Classification from Labeled and Unlabeled Documents using EM. Machine Learning 39(2), 103–134 (2000)
Rasmussen, C.E., Williams, C.K.I.: Gaussian Process for Machine Learning. MIT Press (2006)
Rwebangira, M.R., Lafferty, J.: Local Linear Semi-supervised Regression. Technical Report CMU-CS-09-106, School of Computer Science, Carnegie Mellon University (2009)
Sindhwani, V., Niyogi, P., Belkin, M.: Beyond the Point Cloud: from Transductive to Semi-supervised Learning. In: Proc. of the 22nd International Conference on Machine Learning, pp. 824–831 (2005)
Sindhwani, V., Chu, W., Keerthi, S.S.: Semi-supervised Gaussian Process Classifiers. In: Proc. of the 20th International Joint Conference on Artificial Intelligence, pp. 1059–1064 (2007)
Sindhwani, V., Niyogi, P., Belkin, M.: A Co-regularized Approach to Semi-supervised Learning with Multiple Views. In: Proc. of ICML Workshop on Learning with Multiple Views, pp. 74–79 (2005)
Zhou, Z.H., Li, M.: Semi-supervised Regression with Co-training Style Algorithms. IEEE Transactions on knowledge and Data Engineering 19(11), 1479–1493 (2007)
Zhu, X., Ghahramani, Z., Lafferty, J.: Semi-supervised Learning using Gaussian Fields and Harmonic Functions. In: Proc. of the 20th International Conference on Machine Learning, pp. 912–919 (2003)
Zhu, X., Lafferty, J.D., Ghahramani, Z.: Semi-Supervised Learning: from Gaussian Fields to Gaussian Processes. Technical Report CMU-CS-03-175, School of Computer Science, Carnegie Mellon University (2003)
Zhu, X.: Semi-supervised Learning Literature Survey. Technical Report 1530, Department of Computer Sciences, University of Wisconsin, Madison (2005)
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Guo, X., Yasumura, Y., Uehara, K. (2012). Semi-supervised Gaussian Process Regression and Its Feedback Design. In: Zhou, S., Zhang, S., Karypis, G. (eds) Advanced Data Mining and Applications. ADMA 2012. Lecture Notes in Computer Science(), vol 7713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35527-1_30
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DOI: https://doi.org/10.1007/978-3-642-35527-1_30
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