Outlier detection via localized p-value estimation

2009 47th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2009

Volumn , Issue , 2009, Pages 1482-1489

  Zhao, Manqi ^a   Saligrama, Venkatesh ^a  

^a Boston University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADDITIONAL COSTS; ASYMPTOTICALLY OPTIMAL; COMPUTATIONALLY EFFICIENT; DATA SIZE; FALSE ALARMS; FUNCTION APPROXIMATION; HIGH DIMENSIONAL DATA; HIGH-DIMENSIONAL FEATURE SPACE; LOCAL STRUCTURE; MANIFOLD LEARNING; NEAREST NEIGHBORS; NON-PARAMETRIC; OUTLIER DETECTION; OUTLIER DETECTION ALGORITHM; P-VALUES; REAL DATA SETS; RECEIVING OPERATING CHARACTERISTICS; SCORE FUNCTION; TEST SAMPLES; TUNING PARAMETER;

CLUSTERING ALGORITHMS; DATA MINING; DETECTORS; ERRORS;

ADAPTIVE ALGORITHMS;

EID: 77949580685     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ALLERTON.2009.5394501     Document Type: Conference Paper

References (21)

1. C. Campbell and K. P. Bennett, "A linear programming approach to novelty detection," in Advances in Neural Information Processing Systems 13. MIT Press, 2001, pp. 395-401.
2. M. Markou and S. Singh, "Novelty detection: a review - part 1: statistical approaches," Signal Processing, vol. 83, pp. 2481-2497, 2003.
3. A. O. Hero, "Geometric entropy minimization(GEM) for anomaly detection and localization," in Neural Information Processing Systems Conference, vol. 19, 2006.
4. R. Vert and J. Vert, "Consistency and convergence rates of one-class svms and related algorithms," Journal of Machine Learning Research, vol. 7, pp. 817-854, 2006.
5. D. Tax and K. R. Müller, "Feature extraction for one-class classification," in Artificial neural networks and neural information processing, Istanbul, TURQUIE, 2003.
6. R. El-Yaniv and M. Nisenson, "Optimal singl-class classification strategies," in Advances in Neural Information Processing Systems 19. MIT Press, 2007.
7. I. V. Nikiforov and M. Basseville, Detection of abrupt changes: theory and applications. Prentice-Hall, New Jersey, 1993.
8. R. Ramaswamy, R. Rastogi, and K. Shim, "Efficient algorithms for mining outliers from large data sets," in Proceedings of the ACM SIGMOD Conference, 2000.
9. K. Zhang, M. Hutter, and H. Jin, "A new local distance-based outlier detection approach for scattered real-world data," March 2009, arXiv:0903.3257v1[cs.LG].
10. B. Schölkopf, J. C. Platt, J. Shawe-Taylor, A. J. Smola, and R. Williamson, "Estimating the support of a high-dimensional distribution," Neural Computation, vol. 13, no. 7, pp. 1443-1471, 2001.
11. D. Tax, "One-class classification: Concept-learning in the absence of counter-examples," Ph.D. dissertation, Delft University of Technology, June 2001.
12. G. R. G. Lanckriet, L. E. Ghaoui, and M. I. Jordan, "Robust novelty detection with single-class MPM," in Neural Information Processing Systems Conference, vol. 18, 2005.
13. C. Scott and R. D. Nowak, "Learning minimum volume sets," Journal of Machine Learning Research, vol. 7, pp. 665-704, 2006.
14. J. B. Tenenbaum, V. de Silva, and J. C. Langford, "A global geometric framework fo nonlinear dimensionality reduction," Science, vol. 290, pp. 2319-2323, 2000.
15. M. Bernstein, V. D. Silva, J. C. Langford, and J. B. Tenenbaum, "Graph approximations to geodesics on embedded manifolds," 2000.
16. S. T. Roweis and L. K. Saul, "Nonlinear dimensionality reduction by local linear embedding," Science, vol. 290, pp. 2323-2326, 2000.
17. M. Zhao and V. Saligrama, "Anomaly detection with score functions based on nearest neighbor graphs," in Neural Information Processing Systems Conference, vol. 22, 2009.
18. C. H. Papadimitriou and K. Steiglitz, Combinaorial Optimization: Algorithms and Complexity. Dover Publications, Inc., 1998.
19. "Benchmark repository." [Online]. Available: http://ida.first.fhg.de/projects/bench/benchmarks.htm
20. A. Asuncion and D. J. Newman, "UCI machine learning repository," 2007. [Online]. Available: http://www.ics.uci.edu/~mlearn/ MLRepository.html
21. E. B. Ermis and V. Saligrama, "Distributed detection in sensor networks with limited range multi-modal sensors," September 2008, arXiv:0809.1900v1 [cs.IT].