Computationally Efficient Tree Variants of Gromov-Wasserstein
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We propose two novel variants of Gromov-Wasserstein (GW) between probability measures in different probability spaces based on projecting these measures into the tree metric spaces. Our first proposed discrepancy, named flow-based tree Gromov-Wasserstein, hinges upon the tree metric from node to root in each tree to define the structure representation of probability measures on trees. The flow-based tree GW shares similar structures with univariate Wasserstein distance while keeping sufficient spatial information of the original projected probability measures. In order to further explore the structure of tree, we proposed another version of flow-based tree GW, which we refer to as depth-based tree Gromov-Wasserstein. That discrepancy considers the alignment of probability measures hierarchically along each depth level of the tree structures. Finally, we demonstrate via extensive simulation studies on large-scale real data sets the relative advantage of the proposed discrepancies.
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