伪特权信息和SVM+

doi:10.3969/j.issn.1001-2400.2016.06.018

Abstract

Abstract:

In machine learning, learning using privileged information(LUPI) tries to improve the generalization of the classifier by leveraging information only available during learning. In the scenario of privileged information(PI) possessed by partial training samples, pseudo-privileged information(PPI) and SVM+are investigated. The proposed models depend on two formulations. One is to construct PPI for the samples without PI alone. The formulation enables slacks of such samples predicted in the correcting space with an ultimate goal of improving the generalization of the classifier. Available information and random features are proved to be effective options for PPI. The other is to replace the genuine PI with PPI so as to predict the slacks of all training samples in the unique correcting space. It is confirmed that at least for certain genuine PI and two categories classification task, the latter one is capable of obtaining better generalization performance. Experiments are performed on written digits and facial expression recognition. The results have validated advantages of SVM+using PPI.

Key words: learning using privileged information, pseudo-privileged information, SVM+

SUN Guangling;DONG Yong;LIU Zhi. Pseudo-privileged information and SVM+[J].Journal of Xidian University, 2016, 43(6): 103-108.

References

［1］ VAPNIK V, VASHIST A, PAVLOVITCH N. Learning Using Hidden Information(Larning with Teacher)［C］//Proceedings of the International Joint Conference on Neural Networks. Piscataway: IEEE, 2009: 3188-3195.
［2］ VAPNIK V, VASHIST A. A New Learning Paradigm: Learning Using Privileged Information［J］. Neural Networks, 2009, 22(5/6): 544-557.
［3］ FEYEREISL J, AICKELIN U. Privileged Information for Data Clustering［J］. Information Sciences, 2012, 194: 4-23.
［4］ RIBEIRO B, SILVA C, VIEIRA A, et al. Financial Distress Model Prediction Using SVM+［C］//Proceedings of the International Joint Conference on Neural Networks. Piscataway: IEEE, 2010: 5596729.
［5］ PECHYONY D, VAPNIK V. On the Theory of Learnining with Privileged Information［C］//Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems. Vancouver: Neural Information Processing Systems Foundation, 2010: 1894-1902.
［6］ 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.
［7］ HERNNDEZ-LOBATO D, SHARMANSKA V, KERSTING K, et al. Mind the Nuisance: Gaussian Process Classification using Privileged Noise［C］//Advances in Neural Information Processing Systems 27: 28th Annual Conference on Neural Information Processing Systems.Vancouver: Neural Information Processing Systems Foundation, 2014: 837-845.
［8］ LAPIN M, HEIN M, SCHIELE B. Learning Using Privileged Information: SVM+and Weighted SVM［J］. Neural Networks, 2014, 53: 95-108.
［9］ FEYEREISL J, KWAK S, SON J, et al. Object Localization Based on Structural SVM Using Privileged Information［C］//Advances in Neural Information Processing Systems27: 28th Annual Conference on Neural Information Processing Systems. Vancouver: Neural Information Processing Systems Foundation, 2014: 208-216.
［10］ 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.
［11］ SHARMANSKA V, QUADRIANTO N, LAMPERT C H. Learning to Rank Using Privileged Information［C］//Proceedings of the IEEE International Conference on Computer Vision. Piscataway: IEEE, 2013: 825-832.
［12］ 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.
［13］ LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based Learning Applied to Document Recognition［J］. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
［14］ SAVRAN A, ALYZ N, DIBEKLIOLU H, et al. Bosphorus Database for 3D Face Analysis［M］. Lecture Notes in Computer Science:5372. Heidelberg: Springer Verlag, 2008: 47-56.
［15］ AHONEN T, HADID A, PIETIKAINEN M. Face Description with Local Binary Patterns: Application to Face Recognition［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(12): 2037-2041.

Pseudo-privileged information and SVM+

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