In statistical pattern recognition,
parameters of distributions are
usually estimated from training sample vectors.
However, estimated parameters contain estimation errors and
the errors cause bad influence on recognition
performance when the sample size is not sufficient.
In this paper, we present a method to estimate the true Mahalanobis
distance from sample eigenvectors
(eigenvectors of sample covariance matrix)
by considering the errors of eigenvectors.
Recognition experiments show that by applying the proposed method,
the true Mahalanobis distance can be estimated
even if the sample size is small,
and better recognition accuracy is achieved.
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