Abstract
In one-class classification one tries to describe a class of target data and to distinguish it from all other possible outlier objects. Obvious applications are areas where outliers are very diverse or very difficult or expensive to measure, such as in machine diagnostics or in medical applications. In order to have a good distinction between the target objects and the outliers, good representation of the data is essential. The performance of many one-class classifiers critically depends on the scaling of the data and is often harmed by data distributions in (nonlinear) subspaces. This paper presents a simple preprocessing method which actively tries to map the data to a spherical symmetric cluster and is almost insensitive to data distributed in subspaces. It uses techniques from Kernel PCA to rescale the data in a kernel feature space to unit variance. This transformed data can now be described very well by the Support Vector Data Description, which basically fits a hypersphere around the data. The paper presents the methods and some preliminary experimental results.
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References
Bishop, C.: Neural Networks for Pattern Recognition. Oxford University Press, Walton Street, Oxford OX2 6DP (1995)
Tax, D.: One-class classification. PhD thesis, Delft University of Technology, http://www.ph.tn.tudelft.nl/~davidt/thesis.pdf (2001)
Moya, M., Hush, D.: Network contraints and multi-objective optimization for one-class classification. Neural Networks 9 (1996) 463–474
Ritter, G., Gallegos, M.: Outliers in statistical pattern recognition and an application to automatic chromosome classification. Pattern Recognition Letters 18 (1997) 525–539
Bishop, C.: Novelty detection and neural network validation. IEE Proceedings on Vision, Image and Signal Processing. Special Issue on Applications of Neural Networks 141 (1994) 217–222
Japkowicz, N., Myers, C., Gluck, M.: A novelty detection approach to classification. In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence. (1995) 518–523
Tarassenko, L., Hayton, P., Brady, M.: Novelty detection for the identification of masses in mammograms. In: Proc. of the Fourth International IEE Conference on Artificial Neural Networks. Volume 409. (1995) 442–447
Surace, C., Worden, K., Tomlinson, G.: A novelty detection approach to diagnose damage in a cracked beam. In: Proceedings of SPIE. (1997) 947–943
Vapnik, V.: Statistical Learning Theory. Wiley (1998)
Schölkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J.: SV estimation of a distribution’s support. In: Advances in Neural Information Processing Systems. (1999)
Campbell, C., Bennett, K. P.: A linear programming approach to novelty detection. In: NIPS. (2000) 395–401
Scholkopf, B., Smola, A. J., Müller, K. R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10 (1998) 1299–1319
Smola, A.: Learning with kernels. PhD thesis, Technischen University Berlin (1998)
Cho, S. B.: Recognition of unconstrained handwritten numerals by doubly self-organizing neural network. In: International Cconference on Pattern Recognition. (1996)
Metz, C.: Basic principles of ROC analysis. Seminars in Nuclear Medicine VIII (1978)
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© 2002 Springer-Verlag Berlin Heidelberg
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Tax, D.M.J., Juszczak, P. (2002). Kernel Whitening for One-Class Classification. In: Lee, SW., Verri, A. (eds) Pattern Recognition with Support Vector Machines. SVM 2002. Lecture Notes in Computer Science, vol 2388. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45665-1_4
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DOI: https://doi.org/10.1007/3-540-45665-1_4
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