R1SVM: A randomised nonlinear approach to large-scale anomaly detection

Proceedings of the National Conference on Artificial Intelligence

Volumn 1, Issue , 2015, Pages 432-438

  Erfani, Sarah M ^a   Baktashmotlagh, Mahsa ^a   Rajasegarar, Sutharshan ^a   Karunasekera, Shanika ^a   Leckie, Chris ^a  

^a UNIVERSITY OF MELBOURNE   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; MULTIOBJECTIVE OPTIMIZATION; SUPPORT VECTOR MACHINES;

EMPIRICAL ANALYSIS; LARGE-SCALE DATASETS; ONE-CLASS SUPPORT VECTOR MACHINE; RANDOM PROJECTION METHODS; RANDOM PROJECTIONS; SYNTHETIC DATASETS; TRAINING AND TESTING; UNSUPERVISED ANOMALY DETECTION;

SIGNAL DETECTION;

EID: 84959524841     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (33)

1. Achlioptas, D.; McSherry, F.; and Schölkopf, B. 2002. Sampling techniques for kernel methods. In Proceedings of Advances in Neural Information Processing Systems (NIPS), volume 1, 335.
2. Bengio, Y., and LeCun, Y. 2007. Scaling learning algorithms towards AI. Large-Scale Kernel Machines 34.
3. Blum, A. 2006. Random projection, margins, kernels, and feature-selection. In Subspace, Latent Structure and Feature Selection. 52-68.
4. Bottou, L., and Lin, C.-J. 2007. Support vector machine solvers. Large scale kernel machines 301-320.
5. Cao, L. J.; Chua, K. S.; Chong, W. K.; Lee, H. P.; and Gu, Q. M. 2003. A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine. Neurocomputing 55(1):321-336.
6. Dasgupta, S. 2000. Experiments with random projection. In Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence (UAL), 143-151.
7. Fung, G., and Mangasarian, O. L. 2001. Proximal support vector machine classifiers. In Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 77-86.
8. Fung, G., and Mangasarian, O. L. 2003. Finite Newton method for Lagrangian Support Vector Machine classification. Neurocomputing 55(1):39-55.
9. Hamid, R.; Xiao, Y; Gittens, A.; and DeCoste, D. 2014. Compact random feature maps. In Proceedings of 31st International Conference on Machine Learning (ICML).
10. Joachims, T. 1999. Making large scale SVM learning practical. Advances in Kernel Methods - Support Vector Learning.
11. Kar, P., and Karnick, H. 2012. Random feature maps for dot product kernels. In Proceedings of the 15th International Conference on Artificial Intelligence and Statistics (AISTATS), 583-591.
12. Lee, Y.-J., and Mangasarian, O. L. 2001. RSVM: Reduced Support Vector Machines. In Proceedings of the SIAM International Conference on Data Mining (SDM), volume 1, 325-361.
13. Lopez-Paz, D.; Sra, S.; Smola, A.; Ghahramani, Z.; and Schölkopf, B. 2014. Randomized nonlinear component analysis. In Proceedings of the 28th International Conference on Machine Learning (ICML).
14. Manevitz, L. M., and Yousef, M. 2002. One-class SVMs for document classification. Journal of Machine Learning Research (JMLR) 2:139-154.
15. Platt, J. C. 1999. Fast training of support vector machines using sequential minimal optimization. In Advances in Kernel Methods, 185-208.
16. Rahimi, A., and Recht, B. 2007. Random features for large-scale kernel machines. In Advances in Neural Information Processing Systems (NIPS), 1177-1184.
17. Rahimi, A., and Recht, B. 2009. Weighted sums of random kitchen sinks: Replacing minimization with randomization in learning. In Advances in Neural Information Processing Systems (NIPS), 1313-1320.
18. Rajasegarar, S.; Leckie, C; Bezdek, J. C; and Palaniswami, M. 2010. Centered hyperspherical and hyperellipsoidal one-class support vector machines for anomaly detection in sensor networks. IEEE Transactions on Information Forensics and Security (IEEE IFS) 5(3):518-533.
19. Schölkopf, B.; Platt, J. C; Shawe-Taylor, J.; Smola, A. J.; and Williamson, R. C. 2001. Estimating the support of a high-dimensional distribution. Neural computation 13(7): 1443-1471.
20. Settles, B. 2010. Active learning literature survey. University of Wisconsin, Madison 52:55-66.
21. Sonnenburg, S.; Ratsch, G.; Schäfer, C; and Schölkopf, B. 2006. Large scale multiple kernel learning. Journal of Machine Learning Research (JMLR) 7:1531-1565.
22. Subasi, A., and Ismail Gursoy, M. 2010. EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Systems with Applications 37(12):8659-8666.
23. Sun, S.-Y; Tseng, C.-L.; Chen, Y; Chuang, S.; and Fu, H. 2004. Cluster-based support vector machines in text-independent speaker identification. In Proceedings of the IEEE International Joint Conference on Neural Networks (1JCNN), volume 1.
24. Tax, D. M., and Duin, R. P. 2004. Support Vector Data Description. Machine Learning 54:45-66.
25. Tax, D. 2013. DDtools, the Data Description toolbox for Matlab. Version 2.0.2.
26. Tong, S., and Koller, D. 2002. Support vector machine active learning with applications to text classification. Journal of Machine Learning Research (JMLR) 2:45-66.
27. Vapnik, V. N. 1998. Statistical learning theory.
28. Vempati, S.; Vedaldi, A.; Zisserman, A.; and Jawahar, C. 2010. Generalized RBF feature maps for Efficient Detection. In Proceedings of the 21st British Machine Vision Conference, 1-11.
29. Vishwanathan, S.; Smola, A. J.; and Murty, M. N. 2003. Simplesvm. In Proceedings of the International Conference n Machine Learning (ICML), 760-767.
30. Wang, L.; Nguyen, U. T.; Bezdek, J. C; Leckie, C; and Ramamohanarao, K. 2010. i VAT and aVAT: enhanced visual analysis for cluster tendency assessment. In Advances in Knowledge Discovery and Data Mining. 16-27.
31. Wang, D.; Yeung, D. S.; and Tsang, E. C. 2006. Structured one-class classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 36(6): 1283-1295.
32. Yang, C; Duraiswami, R.; and Davis, L. S. 2005. Efficient kernel machines using the improved fast Gauss transform. In Advances in Neural Information Processing Systems, 1561-1568.
33. Zimek, A.; Schubert, E.; and Kriegel, H.-P. 2012. A survey on unsupervised outlier detection in high-dimensional numerical data. Statistical Analysis and Data Mining 5(5):363-387.