SVM+模型中可用信息用作特权信息

doi:10.12066/j.issn.1007-2861.1675

Abstract

Abstract:

In machine learning, some information is only available during learning phase. Learning using privileged information (LUPI) can provide an effective solution to the problem. Such information is called privileged information. To deal with the issue that only partial privileged information of training data is available, this paper presents an intuitive but effective strategy called extended support vector machine+ (eSVM+). Specifically, for data without privileged information, available information is used as privileged information simultaneously and further cooperate it into the SVM+ formulation. In addition, for a regular supervised learning paradigm, a similar idea is adopted that all training data are both available and privileged. Naturally, it is extended SVM (eSVM), and also a special aspect of SVM+. Experimental results show that the proposed strategy can boost generalization performance of the classifier on two benchmark expression databases, BU-3DFE and Bosphorus.

Key words: available information, privileged information, support vector machine+ (SVM+), learning using privilegend information (LUPI)

DONG Yong, SUN Guangling, LIU Zhi. Using available information as privileged information in SVM+[J]. Journal of Shanghai University（Natural Science Edition）, 2017, 23(4): 524-534.

References

[1] Vapnik V, Vashist A, Pavlovitch N. Learning using hidden information (learning with teacher) [C]//International Joint Conference on Neural Networks. 2009.
[2] Vapnik V, Vashist A. A new learning paradigm: learning using privileged information [J]. Neural Networks, 2009, 22(5/6): 544-557.
[3] Christoph L. Learning with asymmetric information [EB/OL]. [2015-09-01]. http://pub.ist.ac.at/~chl/talks/lampert-us2014.pdf.
[4] Jan F, Uwe A. Privileged information for data clustering [J]. Information Sciences, 2012, 194: 4-23.
[5] Ribeiro B, Silva C, Vieira A, et al. Financial distress model prediction using SVM+ [C]//International Joint Conference on Neural Networks. 2010: 1-7.
[6] Pechyony D, Vapnik V. On the theory of learning with privileged information [C]//NIPS. 2010: 1894-1902.
[7] Xu X X, Li W, Xu D. Distance metric learning using privileged information for face verification and person re-identification [J]. IEEE Transactions on Neural Networks and Learning Systems,2015, 26(12): 3150-3162.
[8] Hernandez-Lobato D, Sharmanska V, Kersting K, et al. Mind the nuisance: Gaussian process classification using privileged noise [J]. Advances in Neural Information Processing Systems, 2014(1): 837-845.
[9] Lapin M, Hein M, Schiele B. Learning using privileged information: SVM+ and weighted SVM [J]. Neural Networks, 2014, 53: 95-108.
[10] Feyereisl J, Kwak S, Son J, et al. Object localization based on structural SVM using privileged information [J]. Advances in Neural Information Processing Systems, 2014(1): 208-216.
[11] Yang H, Patras I. Privileged information based conditional structured output regression forest for facial point detection [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2015, 25(9): 1507-1520.
[12] Viktoriia S, Novi Q, Christoph H, et al. Learning to rank using privileged information [C]//ICCV. 2013: 825-832.
[13] Wang S Z, Tao D C, Yang J. Relative attribute SVM+ learning for age estimation [J]. IEEE Transactions on Cybernetics, 2016, 46(3): 827-839.
[14] Pechyony D, Vapnik V. Fast optimization algorithms for solving SVM+ [EB/OL]. [2015-09-05]. http://www.cs.technion.ac.il/pechyony/book chapter.pdf.
[15] Viktoriia S, Novi Q, Christoph L. Learning to transfer privileged information [J]. Computer Science, 2014, arXiv: 1410.0389v1.
[16] Valstar M, Girard J, Almaev T, et al. Fera 2015-second facial expression recognition and analysis challenge [C]// IEEE International Conference on Automatic Face and Gesture Recognition. 2015: 1-18.
[17] Yin L, Wei X, Wang J, et al. A 3D facial expression database for facial behavior research [C]//IEEE International Conference on Automatic Face and Gesture Recognition. 2006: 211-216.
[18] Savran A. Bosphorus database for 3D face analysis [C]//Proc COST Workshop Biometrics Identity Manag. 2008: 47-56.
[19] Ahonen T, Hadid A, Pietikainen M. Face description with local binary patterns: application to face recognition [J]. IEEE T-PAMI, 2006, 28(12): 2037-2041.

Using available information as privileged information in SVM+

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