Efficient support vector data descriptions for novelty detection

Neural Computing and Applications

Volumn 21, Issue 8, 2012, Pages 2023-2032

  Peng, Xinjun ^a   Xu, Dong ^a  

^a SHANGHAI NORMAL UNIVERSITY   (China)

Author keywords

Kernel method; Novelty detection; Partitioning entropy based KFCM; Preimage; Support vector data description

Indexed keywords

KERNEL METHODS; NOVELTY DETECTION; PARTITIONING-ENTROPY-BASED KFCM; PREIMAGES; SUPPORT VECTOR DATA DESCRIPTION;

ENTROPY;

EID: 84867736801     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-011-0625-3     Document Type: Article

References (46)

1. Markou M, Singh S (2003) Novelty detection: a review, part I: statistical approaches. Signal Process 83(12): 2481-2497.
2. Markou M, Singh S (2003) Novelty detection: a review, part II: neural network based approaches. Signal Process 83(12): 2499-2521.
3. Lee K, Kim D, Lee K, Lee D (2007) Density-induced support vector data desciption. IEEE Trans Neural Netw 18(1): 284-289.
4. Vapnik V (1995) The natural of statistical learning theory. Springer, New York.
5. Vapnik V (1998) Statistical learning theory. Wiley, New York.
6. Campbell C, Bennett K (2001) A linear programming approach to novelty detection. In: Advances in neural information processing systems, vol 13. MIT Press, Cambridge, MA.
7. Tax D, Duin R (1999) Support vector domain description. Pattern Recogn Lett 20: 1191-1199.
8. Tax D, Duin R (2004) Support vector data description. Mach Learn 54: 45-66.
9. Roberts S, Tarassenko L (1994) A probabilistic resource allocation network for novelty detection. Neural Comput Appl 6: 270-284.
10. Crammer K, Chechik G (2004) A needle in a haystack: local one-class optimization. In: Proceedings of 21th international conference on machine learning, p 26.
11. Hoffmann H (2007) Kernel PCA for novelty detection. Pattern Recogn Lett 40(3): 863-874.
12. Lanckriet G, Ghaoui L, Jordan M (2003) Robust novelty detection with single-class MPM. In: Advances in neural information processing systems, vol 15. MIT Press, Cambridge, MA, pp 92-936.
13. Schölkopf B, Mika S, Burges C, Knirsch P, Müller K, Rätsch G, Smola A (1999) Input space vs. feature space in kernel-based methods. IEEE Trans Neural Netw 10(5): 1000-1017.
14. Schölkopf B, Smola A (2002) Learning with kernels. MIT Press, Cambridge.
15. Scott C, Nowak R (2006) Learning minimum volume sets. J Mach Learn Res 7: 665-704.
16. Steinwart I, Hush D, Scovel C (2005) A classification framework for anomaly detection. J Mach Learn Res 6: 211-232.
17. Vert R, Vert J (2006) Consistency and convergence rates of one-class SVM and related algorithms. J Mach Learn Res 7: 817-854.
18. Liu Y, Lin S, Hsueh Y, M. L. L (2009) Automatic target defect identification for TFT-LCD array process inspection using kernel FCM-based fuzzy SVDD ensemble. Expert Syst Appl 36(2): 1978-1998.
19. Park J, Kang D, Kim J, Kwok J, Tsang I (2007) Svdd-baded pattern denoising. Neural Comput Appl 19(7): 1919-1938.
20. Nanni L (2006) Machine learning algorithms for T-cell epitopes prediction. Neurocomputing 69(7-9): 866-868.
21. Banerjee A, Burlina P, Diehl C (2006) A support vector method for anomaly detection in hyperspectral imagery. IEEE Trans Geosci Remote Sens 44(8): 2282-2291.
22. Burges C (1996) Simplified support vector decision rules. In: Proceedings of the 13th international conference on machine learning, pp 71-77.
23. Thies T, Weber F (2004) Optimal reduced-set vectors for support vector machines with a quadratic kernel. Neural Comput Appl 16: 1769-1777.
24. Downs T, Gates K, Masters A (2002) Exact simplification of support vector solutions. J Mach Learn Res 2: 293-297.
25. Guo J, Takahashi N, Nishi T (2005) A learning algorithm for improving the classification speed of support vector machines. In: Proceedings of 2005 European conference on circuit theory and design.
26. Jiao L, Bo L, Wang L (2007) Fast sparse approximation for least squares support vector machine. IEEE Trans Neural Netw 18: 685-697.
27. Suykens J, Lukas L, van Dooren P, De Moor B, Vandewalle J (1999) Least squares support vector machine classifiers: a large scale algorithm. In: Proceedings of European conference of circuit theory design, pp 839-842.
28. Suykens J, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3): 293-300.
29. Bo L, Wang L, Jiao L (2007) Selecting a reduced set for building sparse support vector regression in the primal. In: Advances in knowledge discovery and data mining, pp 35-46.
30. Liang X, Chen R, Guo X (2008) Pruning support vector machines without altering performances. IEEE Trans Neural Netw 19(10): 1792-1803.
31. Li Q, Jiao L, Hao Y (2007) Adaptive simplification of solution for support vector machine. Pattern Recogn Lett 40: 972-980.
32. Joachims T, Yu C (2009) Sparse kernel SVMs via cutting-plane training. Mach Learn 76(2-3): 179-193.
33. Liu Y, Liu Y, Chen Y (2010) Fast support vector data description for novelty detection. IEEE Trans Neural Netw 21(8): 1296-1313.
34. Mercer J (1909) Functions of positive and negative type and the connection with the theory of integal equations. Philos Trans R Soc Lond Ser A 209: 415-446.
35. Bakir G, Weston J, Schölkopf B (2004) Learning to find pre-images. In: Thrun J, Saul L, Schölkopf B (eds) Advances in neural information processing systems. vol 16, MIT Press, Cambridge, MA, pp 449-456.
36. Burges C, Schölkopf B (1997) Improving the accuracy and speed of support vector learning machines. In: Mozer M, Jordan M, Petsche T (eds) Advances in neural information processing systems. vol 9, MIT Press, Cambridge, MA, pp 375-381.
37. Kwok J, Tsang I (2004) The pre-image problem in kernel methods. IEEE Trans Neural Netw 15(6): 1517-1525.
38. Mika S, Schölkopf B, Smola A, Müller K, Scholz M, Rätsch G (1998) Kernel pca and de-noising in feature space. In: Kearns M, Solla S, Cohn D (eds) Advances in neural information processing systems, vol 11. Morgan Kaufmann, San Mateo, CA.
39. Bezdek J (1981) Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York.
40. Wu Z, Xie W (2003) Fuzzy c-means clustering algorithm based on kernel method. In: Proceedings of fifth international conference on computational intelligence and multimedia applications, pp 49-54.
41. Lin C, Lee C (1996) Neural fuzzy systems: a neuro-fuzzy synergism to intelligent system. Prentice-Hall, Englewood Cliffs, NJ.
42. Höppner F, Klawonn F, Kruse R, Runkler T (1999) Fuzzy cluster analysis: methods for classification, data analysis and image recognition. Wiley, New York.
43. Huang H, Liu Y (2002) Fuzzy support vector machines for pattern recognition and data mining. Int J Fuzzy Syst 4: 826-835.
44. Meyer C (2000) Matrix analysis and applied linear algebra. SIAM, Philadelphia.
45. Kubat M, Matwin S (1997) Addressing the curse of imbalanced training sets: one-sided selection. In: Proceedings of 14th international conference on machine learning.
46. Wu G, Chang E (2003) Class-boundary alignment for imbalanced dataset learning. In: Proceedings of international conference on machine learning workshop learning from imbalanced datasets.