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.