Statistics for characterizing data on the periphery [electronic resource].

Published:: 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

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Summary:

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
la-ur-10-4665
la-ur-10-04665

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Published through SciTech Connect.
01/01/2010.
"la-ur-10-04665"
" la-ur-10-4665"
IEEE Int'l Geoscience and Remote Sensing Symposium (IGARSS) ; July 30, 2010 ; Honolulu, HI.
Theiler, James P; Hush, Donald R.

Funding Information:

AC52-06NA25396

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