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
fDate :
7/1/1996 12:00:00 AM
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;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
