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October 16, 2025Open Access

Robust Alignment via Partial Gromov-Wasserstein Distances

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The proposed method effectively addresses outlier contamination in dataset alignment.
An estimator based on partial Gromov-Wasserstein distance is introduced for better alignment.
The robustness of the estimator is proven to be optimal in population settings and near-optimal in finite samples.
New structural results on the partial Gromov-Wasserstein distance reveal its operational significance.

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Research demonstrates robust alignment methods via Gromov-Wasserstein distances, suggesting new estimators for contaminated data.

Gong et al. (2025) studied this question.

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