In statistical pattern recognition,
parameters of distributions are
usually estimated from training samples.
It is well known that shortage of training samples causes
estimation errors which reduce recognition accuracy.
By studying estimation errors of eigenvalues,
various methods of avoiding recognition accuracy reduction
have been proposed.
However, estimation errors of eigenvectors have not been considered enough.
In this paper, we investigate estimation errors of eigenvectors
to show these errors are another factor of recognition performance
reduction.
We propose a new method for modifying eigenvalues in order to
reduce bad influence caused by estimation errors of eigenvectors.
Effectiveness of the method is shown by experimental results.
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