Statistics for characterizing data on the periphery [electronic resource].
- Washington, D.C. : United States. Dept. of Energy, 2010.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy.
- Additional Creators:
- Los Alamos National Laboratory, United States. Department of Energy, and United States. Department of Energy. Office of Scientific and Technical Information
- Restrictions on Access:
- Free-to-read Unrestricted online access
- 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.
- Report Numbers:
- E 1.99:la-ur-10-04665
E 1.99: la-ur-10-4665
- Other Subject(s):
- Published through SciTech Connect.
IEEE Int'l Geoscience and Remote Sensing Symposium (IGARSS) ; July 30, 2010 ; Honolulu, HI.
Theiler, James P; Hush, Donald R.
- Funding Information:
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