DocumentCode
1063862
Title
Covariance matrix estimation and classification with limited training data
Author
Hoffbeck, Joseph P. ; Landgrebe, David A.
Author_Institution
AT&T Bell Labs., Whippany, NJ, USA
Volume
18
Issue
7
fYear
1996
fDate
7/1/1996 12:00:00 AM
Firstpage
763
Lastpage
767
Abstract
A new covariance matrix estimator useful for designing classifiers with limited training data is developed. In experiments, this estimator achieved higher classification accuracy than the sample covariance matrix and common covariance matrix estimates. In about half of the experiments, it achieved higher accuracy than regularized discriminant analysis, but required much less computation
Keywords
covariance matrices; maximum likelihood estimation; pattern classification; classification accuracy; classifiers; covariance matrix estimation; limited training data; Analysis of variance; Covariance matrix; Electronic mail; Euclidean distance; Impedance; Labeling; Maximum likelihood estimation; Parameter estimation; Remote sensing; Training data;
fLanguage
English
Journal_Title
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher
ieee
ISSN
0162-8828
Type
jour
DOI
10.1109/34.506799
Filename
506799
Link To Document