Geometric entropy minimization (GEM) for anomaly detection and localization

Advances in Neural Information Processing Systems

Volumn , Issue , 2007, Pages 585-592

  Hero III, Alfred O ^a  

^a UNIVERSITY OF MICHIGAN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANOMALY DETECTION; ANOMALY DETECTOR; GEOMETRIC ENTROPY; HIGH-DIMENSIONAL FEATURE SPACE; K-NEAREST NEIGHBOR GRAPHS; LEAVE-ONE-OUT; LEBESGUE; MINIMUM ENTROPY; NON-PARAMETRIC; P-VALUES; REAL DATA SETS; TEST SAMPLES; TRAINING DATA; TRAINING SAMPLE;

PROBABILITY DISTRIBUTIONS;

ENTROPY;

EID: 51849083081     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (11)

1. A. Hero, B. Ma, O. Michel, and J. Gorman, "Applications of entropic spanning graphs," IEEE Signal Processing Magazine, vol. 19, pp. 85-95, Sept. 2002. www.eecs.umich.edu/~hero/imag-proc.html.
2. A. Hero and O. Michel, "Asymptotic theory of greedy approximations to minimal k-point random graphs," IEEE Trans. on Inform. Theory, vol. IT-45, no. 6, pp. 1921-1939, Sept. 1999.
3. T. S. Ferguson, Mathematical Statistics - A Decision Theoretic Approach. Academic Press, Orlando FL, 1967.
4. I. V. Nikiforov and M. Basseville, Detection of abrupt changes: theory and applications. Prentice-Hall, Englewood-Cliffs, NJ, 1993.
5. B. Scholkopf, R.Williamson, A. Smola, J. Shawe-Taylor, and J. Platt, "Support vector method for novelty detection," in Advances in Neural Information Processing Systems (NIPS), vol. 13, 2000.
6. G. R. G. Lanckriet, L. El Ghaoui, and M. I. Jordan, "Robust novelty detection with single-class mpm," in Advances in Neural Information Processing Systems (NIPS), vol. 15, 2002.
7. C. Scott and R. Nowak, "Learning minimum volume sets," Journal of Machine Learning Research, vol. 7, pp. 665-704, April 2006.
8. A. Lazarevic, A. Ozgur, L. Ertoz, J. Srivastava, and V. Kumar, "A comparative study of anomaly detection schemes in network intrusion detection," in SIAM Conference on data mining, 2003.
9. S. Ramaswamy, R. Rastogi, and K. Shim, "Efficient algorithms for mining outliers from large data sets," in Proceedings of the ACM SIGMOD Conference, 2000.
10. R. Ravi, M. Marathe, D. Rosenkrantz, and S. Ravi, "Spanning trees short or small," in Proc. 5th Annual ACM-SIAM Symposium on Discrete Algorithms, (Arlington, VA), pp. 546-555, 1994.
11. J. E. Yukich, Probability theory of classical Euclidean optimization, vol. 1675 of Lecture Notes in Mathematics. Springer-Verlag, Berlin, 1998.