Abstract

If two vectors originate from the same underlying distribution, the distance between them could be computed with the Mahalanobis distance, a generalization of the Euclidean one. Also, it can be defined as the Euclidean distance computed in the Mahalanobis space. Moreover, there exist also the city block-based Mahalanobis distance and other versions including the angle- and cosine-based ones. Largely employed for face recognition with bi-dimensional facial data, Mahalanobis gains very good performances with PCA algorithms.