New Kernel-Based Normality Recovery Method and Applications

새로운 커널 기반 정상 상태 복구 기법과 응용

  • 강대성 (고려대학교 제어계측공학과) ;
  • 박주영 (고려대학교 제어계측공학과)
  • Published : 2006.08.01


The SVDD(support vector data description) is one of the most important one-class support vector learning methods, which depends on the strategy of utilizing the balls defined on the feature space to discriminate the normal data from all other possible abnormal objects. This paper addresses on the extension of the SVDD method toward the problem of recovering the normal contents from the data contaminated with noises. The validity of the proposed de-noising method is shown via application to recovering the high-resolution images from the low-resolution images based on the high-resolution training data.


  1. N. Cristianini and J. Shawe-Taylor, 'An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods,' Cambridge, U.K.: Cambridge University Press, 2000
  2. B. Scholkopf, and A. J. Smola, 'Learning with Kernels,' Cambridge, MA: MIT Press, 2002
  3. C. Campbell and K. P. Bennett, 'A linear programming approach to novelty detection,' Advances in Neural Information Processing Systems, vol. 13, pp. 395-401, Cambridge, MA: MIT Press, 2001
  4. K. Crammer and G. Chechik, 'A needle in a haystack: Local one-class optimization,' In Proceedings of the Twentieth-First International Conference on Machine Learning, Banff, Alberta, Canada, 2004
  5. G. R. G. Lanckriet, L. El Ghaoui, and M. I. Jordan, 'Robust novelty detection with single-class MPM,' Advances in Neural Information Processing Systems, vol. 15, pp. 905-912, Cambridge, MA: MIT Press, 2003
  6. G. Ratsch, S. Mika, B. Scholkopf, and K. -R.Muller, 'Constructing boosting algorithms from SVMs: An application to one-class classification,' IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, pp. 1-15, 2002
  7. B. Scholkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola, and R. C. Williamson, 'Estimating the support of a high-dimensional distribution,' Neural Computation, vol. 13, pp. 1443-1471, 2001
  8. B. Scholkopf, J. C. Platt, and A. J. Smola, 'Kernel Method for Percentile Feature Extraction,' Technical Report MSR-TR-2000-22, Microsoft Research, 2000
  9. D. Tax, 'One-Class Classification,' Ph.D. Thesis, Delft University of Technology, 2001
  10. D. Tax and R. Duin, 'Support Vector Domain Description,' Pattern Recognition Letters, vol. 20, pp. 1191-1199, 1999
  11. S.-W. Lee, J.-S Park, and S.-W. Hwang, 'How can we reconstruct facial image from partially occluded or low-resolution one?,' Lecture Notes in Computer Science, vol.3338, pp. 386-399, 2004
  12. S. Mika, B. Scholkopf, A. Smola, K. R. Muller, M. Scholz, and G. Ratsch, 'Kernel PCA and de-noising in feature space,' Advances in Neural Information Processing Systems, vol. 11, pp. 536-542, Cambridge, MA: MIT Press, 1999
  13. J.T. Kwok and I. W. Tsang, 'The pre-image problem in kernel methods,' IEEE Transactions on Neural Networks, vol. 15, pp. 1517-1525, 2004
  14. T. F. Cox and M. A. A. Cox, 'Multidimensional Scaling,' Monographs on Statistics and Applied Probability, vol. 88, 2nd Ed., London, U.K.: Chapman and Hall, 2001
  15. C. K. I. Williams, 'On a connection between kernel PCA and metric multidimensional scaling,' Machine Learning, vol. 46, PP. 11-19, 2002
  16. T. Vetter and N.E. Troje, 'Separation of texture and shape in images of faces for image coding and synthesis,' Journal of the Optical Society of America A, vol. 14, pp. 2152-2161, 1997
  17. J. Park, D. Kang, J T. Kwok, S.-W. Lee, B.-W. Hwang, and S.-W. Lee, 'Facial image reconstruction by SVDD-based pattern de-noising,' Lecture Notes in Computer Science, vol. 3832, pp. 129-135, 2005
  18. A. Ben-Hur, D. Horm, H. T. Siegelmann, and V. Vapnik, 'Support vector clustering,' Journal of Machine Learning Research, vol. 2, pp. 125-137, 2001