A modification on Mahalanobis distance metric on samples of limited size is proposed. By the introduction of compensation for errors of non-dominant eigenvalues, one can compute efficiently the nearest neighbor in transformed spaces, with identical normal distribution on every principal component. This paper gives the derivation of the method and presents experimental results on UCI dataset for handwritten digit recognition.
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