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Mahalanobis distance can be combined with KNN search. Swap point distance calculation from euclidean to mahalanobis and you have it.

Another way of viewing mahalanobis distance is it rotates the data space, or have "diagonal" weights. Which is good when classifiers are not completely independent from each other.

i.e. lateral velocity and vertical velocity. If lateral velocity is 8, vertical velocity is always 0. Coordinates where both velocities are 8 are always empty. Mahalanobis distance then uses a "diagonal" weight so 5.6 lateral velocity and 5.6 vertical velocity (5.6, 5.6) is normalized as (8, 8). While (8, 0) is still normalized as (8, 0).