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Covariance matrix estimation and classification with limited training data

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2 Author(s)
J. P. Hoffbeck ; AT&T Bell Labs., Whippany, NJ, USA ; D. A. Landgrebe

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

Published in:

IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:18 ,  Issue: 7 )