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On the condition number of Gaussian sample-covariance matrices

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2 Author(s)
Kostinski, A.B. ; Dept. of Phys., Michigan Technol. Univ., Houghton, MI, USA ; Koivunen, A.C.

The authors examine the reasons behind the fact that the Gaussian autocorrelation-function model, widely used in remote sensing, yields a particularly ill-conditioned sample-covariance matrix in the case of many strongly correlated samples. The authors explore the question numerically and relate the magnitude of the matrix-condition number to the nonnegativity requirement satisfied by all correlation functions. They show that the condition number exhibits explosive growth near the boundary of the allowed-parameter space, simple numerical recipes are suggested in order to avoid this instability

Published in:

Geoscience and Remote Sensing, IEEE Transactions on  (Volume:38 ,  Issue: 1 )

Date of Publication:

Jan 2000

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