Normalized residual-based constant false-alarm rate outlier detection

Pattern Recognition Letters

Volumn 69, Issue , 2016, Pages 1-7

  Ru, Xiaohu ^a   Liu, Zheng ^a   Huang, Zhitao ^a   Jiang, Wenli ^a  

^a NATIONAL UNIVERSITY OF DEFENSE TECHNOLOGY   (China)

Author keywords

Constant false alarm rate (CFAR); Normalized residual; Outlier detection; Supervised

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATA HANDLING; ERRORS; IMAGE RESOLUTION; LEARNING SYSTEMS; STATISTICS;

CONSTANT FALSE ALARM RATE; DETECTION METHODS; NOISE DISTRIBUTION; NORMALIZED RESIDUALS; OPERATING CHARACTERISTICS; OUTLIER DETECTION; SUPERVISED; TRAINING STRATEGY;

ALARM SYSTEMS;

EID: 84946616003     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2015.10.002     Document Type: Article

References (34)

1. Angiulli F., Pizzuti C. Outlier mining in large high-dimensional data sets IEEE Trans. Knowledge and Data Eng. 17 2005 203 215
2. Barnett V., Lewis T. Outliers in Statistical Data third ed. 1994 John Wiley & Sons New York
3. Breunig M.M., Kriegel H-P., Ng R.T., Sander J. LOF: Identifying density-based local outliers Proceedings of ACM SIGMOD International Conference on Management of Data New York, USA 2000 93 104
4. Brodley C.E., Friedl M.A. Identifying mislabeled training data J. Artif. Intell. Res. 11 1999 131 167
5. Chandola V., Banerjee A., Kumar V. Anomaly detection: A survey ACM Comput. Surv. 41 3 2009 1 58 Article 15
6. Chen Y.T., Qian J., Saligrama V. A new one-class SVM for anomaly detection Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) Vancouver, Canada 2013 3567 3571
7. Daneshpazhouh A., Sami A. Entropy-based outlier detection using semi-supervised approach with few positive examples Patt. Recog. Lett. 49 2014 77 84
8. Duncan J., Dabiri D., Hove J., Gharib M. Universal outlier detection for particle image velocimetry (PIV) and particle tracking velocimetry (PTV) data Measur. Sci. Tech. 21 2010 1 5
9. Furlani M., Tuia D., Munoz-Mari J., Bovolo F., Camps-Valls G., Bruzzone L. Discovering single classes in remote sensing images with active learning Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Munich, Germany 2012 7341 7344
10. Gao J., Cheng H., Tan P.N. A novel framework for incorporating labeled examples into anomaly detection Proceedings of the 2nd SIAM International Conference on Data Mining Bethesda, Maryland 2006 593 597
11. Gao J., Cheng H., Tan P.N. Semi-supervised outlier detection Proceedings of ACM Symposium on Applied Computing (SAC) Dijon, France 2006 635 636
12. Ghosh A.K., Chaudhuri P. On maximum depth classifiers Scandinavian J. Stat. 32 2005 327 350
13. Guan D., Yuan W., Lee Y.-K. Identifying mislabeled training data with the aid of unlabeled data Appl. Intell. 35 2011 345 358
14. Hawkins D. Identification of Outliers 1980 Chapman and Hall London
15. He Z., Xu X., Deng S. Discovering cluster-based local outliers Patt. Recogn. Lett. 24 2003 1641 1650
16. Janssens J.H.M., Flesch I., Postma E.O. Outlier detection with one-class classifiers from ML and KDD Proceedings of IEEE International Conference on Machine Learning and Applications (ICMLA) Miami Beach, Florida 2009 147 153
17. Jumutc V., Suykens J.A.K. Multi-class supervised novelty detection IEEE Trans. Patt. Anal. Mach. Intel. 36 12 2014 2510 2523
18. Kay S.M. Fundamentals of Statistical Signal Processing, Volume II: Detection Theory 1993 Prentice Hall New Jersey
19. Lauer M. A mixture approach to novelty detection using training data with outliers Proceedings of 12th European Conference on Machine Learning (ECML) Freiburg, Germany 2001 300 311
20. Lehtomäki J.J. CFAR outlier detection with forward methods IEEE Trans. Signal Proces. 55 9 2007 4702 4706
21. Lichman M. UCI Machine Learning Repository 2013 University of California, School of Information and Computer Science Irvine, CA http://archive.ics.uci.edu/ml
22. Liu J., Wan J., Zheng H., Huang Y.L. A method of specific emitter verification based on CSDA and SVDD proceedings of the 2nd IEEE International Conference on Computer Science and Network Technology Changchun, China 2012 562 565
23. Mangasarian O.L., Wolberg W.H. Cancer diagnosis via linear programming SIAM News 23 5 1990 1 18
24. Miller D.J., Browning J. A mixture model and EM-based algorithm for class discovery, robust classification, and outlier rejection in mixed labeled/unlabeled data sets IEEE Trans. Patt. Anal. Mach. Intel. 25 11 2003 1468 1483
25. Nattorn B., Arthorn L., Krung S. Outlier detection score based on ordered distance difference Proceedings of IEEE International Computer Science and Engineering Conference (ICSEC) Nakhon Pathom, Thailand 2013 157 162
26. Qian J., Saligrama V. New statistic in p-value estimation for anomaly detection Proceedings of IEEE Statistical Signal Processing Workshop (SSP) Ann Arbor, Michigan, USA 2012 393 396
27. Ru X.H., Liu Z., Jiang W.L. Normalized residual-based outlier detection Proceedings of IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) Guilin, China 2014 190 193
28. Schölkopf B., Platt J.C., Shawe-Taylor J.C., Smola A.J., Williamson R.C. Estimating the support of a high-dimensional distribution Neur. Comput. 13 7 2001 1443 1471
29. Stevens M.C. Secondary Surveillance Radar 1988 Artech House Norwood
30. Westerweel J., Scarano F. Universal outlier detection for PIV data Exper. Fluids 39 2005 1096 1100
31. Xue Z., Shang Y., Feng A. Semi-supervised outlier detection based on fuzzy rough C-means clustering Math. Comput. Simul. 80 9 2010 1911 1921
32. Ye H.H., Liu Z., Jiang W.L. Comparison of unintentional frequency and phase modulation features for specific emitter identification Electron. Lett. 48 14 2012 875 876
33. Zhang K., Hutter M., Jin H. A new local distance-based outlier detection approach for scattered real-world data Adv. Knowl. Discovery Data Min. 2009 813 822
34. Zhao M., Saligrama V. Anomaly detection with score functions based on nearest neighbor graphs Adv. Neural Infor. Proces. Syst. 22 2009 1 10