Abstract
Support Vector Data Description (SVDD) as a one-class classifier was developed to construct the minimum hypersphere that encloses all the data of the target class in a high dimensional feature space. However, SVDD treats the features of all data equivalently in constructing the minimum hypersphere since it adopts Euclidean distance metric and lacks the incorporation of prior knowledge. In this paper, we propose an improved SVDD through introducing relevant metric learning. The presented method named RSVDD here assigns large weights to the relevant features and tights the similar data through incorporating the positive equivalence information in a natural way. In practice, we introduce relevant metric learning into the original SVDD model with the covariance matrices of the positive equivalence data. The experimental results on both synthetic and real data sets show that the proposed method can bring more accurate description for all the tested target cases than the conventional SVDD.
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References
Scholkopf, B., Platt, J., Shawe-Taylor, J., Smola, A., Williamson, R.: Estimating the support of a high dimensional distribution. Neural Computation 13(7), 1443–1471 (2001)
Tax, D., Duin, R.: Support vector domain description. Pattern Recognition Letters 20(14), 1191–1199 (1999)
Tax, D., Duin, R.: Support vector data description. Machine Learning 54, 45–66 (2004)
Tax, D., Juszczak, P.: Kernel whitening for one-class classification. International Journal of Pattern Recognition and Artificial Intelligence 17(3), 333–347 (2003)
Shental, N., Hertz, T., Weinshall, D., Pavel, M.: Adjustment learning and relevant component analysis. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 776–790. Springer, Heidelberg (2002)
Alizadeh, F., Goldfarb, D.: Second-order cone programming. Mathematical Programming 95, 3–51 (2003)
Scholkopf, B., Smola, A.: Learning with Kernels. MIT Press, Cambridge (2002)
Tsang, I., Cheung, P., Kwok, J.: Kernel relevant component analysis for distance metric learning. In: Proceeding of the International Joint Conference on Neural Networks (2005)
Golub, G., Van Loan, C.: Matrix Computations, 3rd edn. Johns Hopkins, Baltimore (1996)
Asuncion, A., Newman, D.: Uci machine learning repository. University of California, School of Information and Computer Science, Irvine (2007), http://www.ics.uci.edu/~mlearn/mlrepository.html
Bradley, A.: The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recognition 30(7), 1145–1159 (1997)
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Wang, Z., Gao, D., Pan, Z. (2010). An Effective Support Vector Data Description with Relevant Metric Learning. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13318-3_6
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DOI: https://doi.org/10.1007/978-3-642-13318-3_6
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