Solved – Mahalanobis distance in a LDA classifier

I've read that Mahalanobis distance is as effective as the Euclidean distance when comparing 2 projected feature vectors in classification using a LDA classifier.

I was wondering if this statement were true?

It would be nice if someone could comment on this.

I'm working on projecting a 36 dimensional feature vector to a 1d feature vector using a 2 class LDA classifier and comparing projected feature vectors.

Given that the covariance matrix S = I, the identity matrix, the Mahalanobis distance is equal to the normalised euclidean distance – which is a scale invariant Euclidean distance.

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