Quasi Manhattan Wasserstein Distance
Abstract
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.
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