L1 norm based KPCA for novelty detection

Pattern Recognition

Volumn 46, Issue 1, 2013, Pages 389-396

  Xiao, Yingchao ^a   Wang, Huangang ^a   Xu, Wenli ^a   Zhou, Junwu ^b  

^a TSINGHUA UNIVERSITY   (China)

^b BEIJING GENERAL RESEARCH INSTITUTE OF MINING AND METALLURGY   (China)

Author keywords

KPCA; L1 norm; Novelty detection

Indexed keywords

KPCA; L1 NORM; L2-NORM; NOVELTY DETECTION; ONE-CLASS CLASSIFICATION; OPTIMIZATION PROBLEMS; SIMULATION DATA;

PATTERN RECOGNITION; SOFTWARE ENGINEERING;

STATISTICS;

EID: 84866013812     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2012.06.017     Document Type: Article

References (21)

1. D.A. Clifton, P.R. Bannister, L. Tarassenko, Learning shape for jet engine novelty detection, in: Advances in Neural Networks - Isnn 2006, Pt 3, Proceedings, vol. 3973, 2006, pp. 828-835.
2. C.K. Oh, H. Sohn, and I.H. Bae Statistical novelty detection within the Yeongjong suspension bridge under environmental and operational variations Smart Materials & Structures 18 2009
3. J. Toivola, M.A. Prada, J. Hollmen, Novelty detection in projected spaces for structural health monitoring, in: Advances in Intelligent Data Analysis IX, Proceedings, vol. 6065, 2010, pp. 208-215.
4. S. Roberts, and L. Tarassenko A probabilistic resource allocating network for novelty detection Neural Computation 6 1994 270 284
5. D.Y. Yeung, C. Chow, Parzen-window network intrusion detectors, in: 16th International Conference on Pattern Recognition, vol. IV, Proceedings, 2002, pp. 385-388.
6. C. He, and M. Girolami Novelty detection employing an l(2) optimal non-parametric density estimator Pattern Recognition Letters 25 2004 1389 1397
7. D. Hristozov, T.I. Oprea, and J. Gasteiger Ligand-based virtual screening by novelty detection with self-organizing maps Journal of Chemical Information and Modeling 47 2007 2044 2062
8. L.B. Jack, A.K. Nandi, and M.L.D. Wong Automated novelty detection using a modified Kohonen self organizing map Vibrations in Rotating Machinery 2004 2004 313 322
9. A.L.I. Oliveira, F.R.G. Costa, and C.O.S. Filho Novelty detection with constructive probabilistic neural networks Neurocomputing 71 2008 1046 1053
10. J. Owens, A. Hunter, and E. Fletcher Novelty detection in video surveillance using hierarchical neural networks Artificial Neural Networks - Icann 2002 2415 2002 1249 1254
11. H. Hoffmann Kernel pca for novelty detection Pattern Recognition 40 2007 863 874
12. D.M.J. Tax, and R.P.W. Duin Support vector data description Machine Learning 54 2004 45 66
13. S.M. Guo, L.C. Chen, and J.S.H. Tsai A boundary method for outlier detection based on support vector domain description Pattern Recognition 42 2009 77 83
14. Y.H. Liu, Y.C. Liu, and Y.J. Chen Fast support vector data descriptions for novelty detection IEEE Transactions on Neural Networks 21 2010 1296 1313
15. B. Scholkopf, J.C. Platt, J. Shawe-Taylor, A.J. Smola, and R.C. Williamson Estimating the support of a high-dimensional distribution Neural Computation 13 2001 1443 1471
16. D.M.J. Tax, and P. Juszczak Kernel whitening for one-class classification International Journal of Pattern Recognition and Artificial Intelligence 17 2003 333 347
17. N. Kwak Principal component analysis based on l1-norm maximization IEEE Transactions on Pattern Analysis and Machine Intelligence 30 2008 1672 1680
18. T. Fawcett An introduction to roc analysis Pattern Recognition Letters 27 2006 861 874
19. R. Duin, P. Juszczak, P. Paclik, E. Pekalska, D. De Ridder, D. Tax, S. Verzakov, A Matlab Toolbox for Pattern Recognition 〈 http://www.prtools. org/ 〉, 2007.
20. K.R. Muller, S. Mika, G. Ratsch, K. Tsuda, and B. Scholkopf An introduction to kernel-based learning algorithms IEEE Transactions on Neural Networks 12 2001 181 201
21. G. Lee, and C. Scott Nested support vector machines IEEE Transactions on Signal Processing 58 2010 1648 1660