Quasi Manhattan Wasserstein Distance
Evan Unit Lim
The Quasi Manhattan Wasserstein Distance (QMWD) is a metric designed to
quantify the dissimilarity between two matrices by combining elements of the
Wasserstein Distance with specific transformations. It offers improved time and
space complexity compared to the Manhattan Wasserstein Distance (MWD) while
maintaining accuracy. QMWD is particularly advantageous for large datasets or
situations with limited computational resources. This article provides a
detailed explanation of QMWD, its computation, complexity analysis, and
comparisons with WD and MWD.