Statistics for characterizing data on the periphery
- Los Alamos National Laboratory
We introduce a class of statistics for characterizing the periphery of a distribution, and show that these statistics are particularly valuable for problems in target detection. Because so many detection algorithms are rooted in Gaussian statistics, we concentrate on ellipsoidal models of high-dimensional data distributions (that is to say: covariance matrices), but we recommend several alternatives to the sample covariance matrix that more efficiently model the periphery of a distribution, and can more effectively detect anomalous data samples.
- Research Organization:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC52-06NA25396
- OSTI ID:
- 1023433
- Report Number(s):
- LA-UR-10-04665; LA-UR-10-4665; TRN: US201118%%1023
- Resource Relation:
- Conference: IEEE Int'l Geoscience and Remote Sensing Symposium (IGARSS) ; July 30, 2010 ; Honolulu, HI
- Country of Publication:
- United States
- Language:
- English
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