Question that relates or uses the Mahalanobis distance metric.
The Mahalanobis distance is a measure of the distance between a point P and a distribution D, introduced by P. C. Mahalanobis in 1936. It is a multi-dimensional generalization of the idea of measuring how many standard deviations away P is from the mean of D. This distance is zero if P is at the mean of D, and grows as P moves away from the mean along each principal component axis.
$X=(x_{1}, x_{2},...,x_{N})^{T}$
$\mu=(\mu_{1},\mu_{2},...,\mu_{N})^{T}$
$S$ is the covariance matrix of $X$
The Mahalanobis distance is: $D_{M}(X)=\sqrt{(X-\mu)^{T}S^{-1}(X-\mu)}$
