Fused Gromov-Wasserstein distances are extended with feature selection via Lasso/Ridge regularization or simplex-constrained weights, yielding theoretical bounds, metric properties, and an alternating minimization algorithm.
Computational optimal transport with applications to data sciences.Foundations and Trends® in Machine Learning, 11(5-6):355–607
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Fused Gromov-Wasserstein Distance with Feature Selection
Fused Gromov-Wasserstein distances are extended with feature selection via Lasso/Ridge regularization or simplex-constrained weights, yielding theoretical bounds, metric properties, and an alternating minimization algorithm.