Abstract
To deal with the problem of sensitivity to noise in semi-supervised learning for biometrics, this paper proposes a robust Gaussian-Laplacian Regularized (GLR) framework based on maximum correntropy criterion (MCC), called GLR-MCC, along with its convergence analysis. The half quadratic (HQ) optimization technique is used to simplify the correntropy optimization problem to a standard semi-supervised problem in each iteration. Experimental results show that the proposed GRL-MCC can effectively improve the semi-supervised learning performance and is robust to mislabeling noise and occlusion as compared with GLR.
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References
Wang, F., Zhang, C.S.: Label propagation through linear neighborhoods. IEEE Transactions on Knowledge and Data Engineering 20(1), 55–67 (2008)
Wang, F., Zhang, C.S.: Robust self-tuning semi-supervised learning. Neurocomputing 70, 2931–2939 (2007)
Belkin, M., Matveeva, I., Niyogi, P.: Regularization and Semi-supervised Learning on Large Graphs. In: Shawe-Taylor, J., Singer, Y. (eds.) COLT 2004. LNCS (LNAI), vol. 3120, pp. 624–638. Springer, Heidelberg (2004)
Zhou, D., Bousquet, O., Lal, T.N., Weston, J., Scholkopf, B.: Learning with Local and Global Consistency. In: NIPS 16 (2004)
Zhu, X., Ghahramani, Z., Lafferty, Z.: Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions. In: International Conference on Machine Learning, ICML (2003)
Liu, W., Pokharel, P.P., Principe, J.C.: Correntropy: Properties and application in non-gaussian signal processing. IEEE Transactions on Signal Processing 55(11), 5286–5298 (2007)
He, R., Hu, B.G., Yuan, X.T., Zheng, W.S.: Principal Component Analysis Based on Nonparametric Maximum Entropy. Neurocomputing (2009)
Chapelle, O., Weston, J., Scholkopf, B.: Cluster kernels for semi-supervised learning. Advances in Neural Information Processing System 15, 585–592 (2003)
Chapelle, O., Weston, J., Scholkopf, B.: Cluster kernels for semi-supervised learning. In: NIPS 15 (2003)
Zhou, D., Bousquet, O., Lal, T.N., Weston, J., Scholkopf, B.: Learning with Local and Global Consistency. In: NIPS 16 (2004)
Liu, W.F., Pokharel, P.P., Principe, J.C.: Correntropy: Properties and Applications in Non-Gaussian Signal Processing. IEEE Transactions on Signal Processing 55(11), 5286–5298 (2007)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Rockfellar, R.: Convex analysis. Princeton Press, Upper Saddle River (1970)
He, R., Hu, B.G., Yuan, X.T.: Robust discriminant analysis based on nonparametric maximum entropy. In: Asian Conference on Machine Learning (ACML), NanJing, China (2009)
Nikolova, M., Ng, M.K.: Analysis of half-quadratic minimization methods for signal and image recovery. Society for Industrial and Applied Mathematics 27(3), 937–966 (2005)
Yang, S.H., Zha, H.Y., Zhou, S.K., Hu, B.G.: Variational graph embedding for globally and locally consistent feature extraction. In: ECML PKDD, pp. 538–553 (2009)
Grandvalet, Y., Bengio, Y.: Entropy Regularization (2006)
Yuan, X.T., Hu, B.G.: Robust feature extraction via information theoretic learning. In: International Conference on Machine Learning, ICML (2009)
He, R., Hu, B.G., Zheng, W.S., Guo, Y.Q.: Two-stage Sparse Representation for Robust Recognition on Large-scale Database. In: Twenty-Fourth AAAI Conference on Artificial Intelligence, AAAI-10 (2010)
Belkin, M., Niyogi, P., Sindhwani, V.: Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples. Journal of Machine Learning Research 7(11), 2399–2434 (2006)
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Yang, N., Huang, M., He, R., Wang, X. (2010). Robust Semi-supervised Learning for Biometrics. In: Li, K., Fei, M., Jia, L., Irwin, G.W. (eds) Life System Modeling and Intelligent Computing. ICSEE LSMS 2010 2010. Lecture Notes in Computer Science, vol 6328. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15621-2_51
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DOI: https://doi.org/10.1007/978-3-642-15621-2_51
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15620-5
Online ISBN: 978-3-642-15621-2
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