DocumentCode
2885686
Title
Use of robust estimators in parametric classifiers
Author
Safavian, S. Rasoul ; Landgrebe, David A.
Author_Institution
Purdue Univ., West Lafayette, IN, USA
fYear
1989
fDate
14-17 Nov 1989
Firstpage
356
Abstract
The authors examine several robust estimators of the mean and covariance matrix and their effect on the probability of error in classification. A simple and intuitive iterative approach which weighs each data point as a function of its distance (Mahalanobis distance) is given in the proposed algorithm, which showed good convergence behavior in simulation with artificial data. Some comments on α-ranked (α-trimmed) estimators are presented
Keywords
convergence of numerical methods; iterative methods; parameter estimation; pattern recognition; α-ranked estimators; α-trimmed estimators; Mahalanobis distance; convergence; covariance matrix; error probability; iterative approach; mean matrix; parametric classifiers; robust estimators; Covariance matrix; Design engineering; Equations; Iterative methods; Laboratories; Maximum likelihood estimation; Parameter estimation; Probability distribution; Remote sensing; Robustness;
fLanguage
English
Publisher
ieee
Conference_Titel
Systems, Man and Cybernetics, 1989. Conference Proceedings., IEEE International Conference on
Conference_Location
Cambridge, MA
Type
conf
DOI
10.1109/ICSMC.1989.71316
Filename
71316
Link To Document