Web: http://arxiv.org/abs/2205.05838

May 13, 2022, 1:11 a.m. | Hongwei Jin, Zishun Yu, Xinhua Zhang

cs.LG updates on arXiv.org arxiv.org

Comparing structured data from possibly different metric-measure spaces is a
fundamental task in machine learning, with applications in, e.g., graph
classification. The Gromov-Wasserstein (GW) discrepancy formulates a coupling
between the structured data based on optimal transportation, tackling the
incomparability between different structures by aligning the intra-relational
geometries. Although efficient local solvers such as conditional gradient and
Sinkhorn are available, the inherent non-convexity still prevents a tractable
evaluation, and the existing lower bounds are not tight enough for practical
use. To …


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