Quasi Manhattan Wasserstein Distance
This is an incremental improvement for researchers and practitioners needing efficient matrix dissimilarity metrics in data-intensive applications.
The paper tackled the problem of quantifying dissimilarity between matrices by introducing the Quasi Manhattan Wasserstein Distance (QMWD), which combines elements of Wasserstein Distance with transformations to improve time and space complexity compared to Manhattan Wasserstein Distance while maintaining accuracy, with advantages for large datasets or limited computational resources.
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.