Abstract
Novelty detection arises as an important learning task in several applications. Kernel-based approach to novelty detection has been widely used due to its theoretical rigor and elegance of geometric interpretation. However, computational complexity is a major obstacle in this approach. In this paper, leveraging on the cutting-plane framework with the well-known One-Class Support Vector Machine, we present a new solution that can scale up seamlessly with data. The first solution is exact and linear when viewed through the cutting-plane; the second employed a sampling strategy that remarkably has a constant computational complexity defined relatively to the probability of approximation accuracy. Several datasets are benchmarked to demonstrate the credibility of our framework.
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References
Abbey, A.S., Temitope, O., Lionel, S.: Active platform security through intrusion detection using naive bayesian network for anomaly detection. In: Proceedings of London Communications Symposium (2002)
Augusteijn, M.F., Folkert, B.A.: Neural network classification and novelty detection. International Journal of Remote Sensing 23, 2891–2902 (2002)
Badoiu, M., Clarkson, K.L.: Optimal core-sets for balls. In: Proc. of DIMACS Workshop on Computational Geometry (2002)
Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: A survey. ACM Comput. Surv. 41(3), 1–58 (2009)
Chang, C.-C., Lin, C.-J.: Libsvm: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 27:1–27:27 (2011)
Fan, W., Miller, M., Stolfo, S.J., Lee, W.: Using artificial anomalies to detect unknown and known network intrusions. In: Proceedings of the first IEEE International Conference on Data Mining, pp. 123–130. IEEE Computer Society (2001)
Fawcett, T., Provost, F.: Activity monitoring: noticing interesting changes in behavior. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 53–62 (1999)
Gwadera, R., Atallah, M.J., Szpankowski, W.: Reliable detection of episodes in event sequences. Knowl. Inf. Syst. 7(4), 415–437 (2005)
Joachims, T., Finley, T., Yu, C.-N.J.: Cutting-plane training of structural svms. Machine Learning 77(1), 27–59 (2009)
Joachims, T., Yu, C.-N.J.: Sparse kernel svms via cutting-plane training. Machine Learning 76(2–3), 179–193 (2009)
Kelley, J.E.: The cutting plane method for solving convex programs. Journal of the SIAM 8, 703–712 (1960)
Schölkopf, B., Platt, J.C., Shawe-Taylor, J.C., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Comput. 13(7), 1443–1471 (2001)
Shalev-Shwartz, S., Singer, Y., Srebro, N., Cotter, A.: Pegasos: Primal estimated sub-gradient solver for svm. Mathematical Programming 127(1), 3–30 (2011)
Tax, D.M.J., Duin, R.P.W.: Support vector data description. Journal of Machine Learning Research 54(1), 45–66 (2004)
Teo, C.H., Smola, A., Vishwanathan, SVN, Le, Q.V.: A scalable modular convex solver for regularized risk minimization. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 727–736. ACM (2007)
Teo, C.H., Vishwanthan, S.V.N., Smola, A.J., Le, Q.V.: Bundle methods for regularized risk minimization. The Journal of Machine Learning Research 11, 311–365 (2010)
Tsang, I.W., Kocsor, A., Kwok, J.T.: Simpler core vector machines with enclosing balls. In: Proceedings of the 24th International Conference on Machine Learning, ICML 2007, pp. 911–918 (2007)
Tsang, I.W., Kwok, J.T., Cheung, P., Cristianini, N.: Core vector machines: Fast svm training on very large data sets. Journal of Machine Learning Research 6, 363–392 (2005)
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© 2015 Springer International Publishing Switzerland
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Le, T., Phung, D., Nguyen, K., Venkatesh, S. (2015). Fast One-Class Support Vector Machine for Novelty Detection. In: Cao, T., Lim, EP., Zhou, ZH., Ho, TB., Cheung, D., Motoda, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2015. Lecture Notes in Computer Science(), vol 9078. Springer, Cham. https://doi.org/10.1007/978-3-319-18032-8_15
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DOI: https://doi.org/10.1007/978-3-319-18032-8_15
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