Linear Partial Gromov-Wasserstein Embedding
The Gromov Wasserstein (GW) problem, a variant of the classical optimal transport (OT)
problem, has attracted growing interest in the machine learning and data science …
problem, has attracted growing interest in the machine learning and data science …
Generalized Dimension Reduction Using Semi-Relaxed Gromov-Wasserstein Distance
RA Clark, T Needham, T Weighill - arxiv preprint arxiv:2405.15959, 2024 - arxiv.org
Dimension reduction techniques typically seek an embedding of a high-dimensional point
cloud into a low-dimensional Euclidean space which optimally preserves the geometry of …
cloud into a low-dimensional Euclidean space which optimally preserves the geometry of …
A dimensionality reduction technique based on the Gromov-Wasserstein distance
Analyzing relationships between objects is a pivotal problem within data science. In this
context, Dimensionality reduction (DR) techniques are employed to generate smaller and …
context, Dimensionality reduction (DR) techniques are employed to generate smaller and …
A Graph Matching Approach to Balanced Data Sub-Sampling for Self-Supervised Learning
H Van Assel, R Balestriero - … Self-Supervised Learning-Theory and Practice - openreview.net
Real-world datasets often display inherent imbalances in the distribution of classes or
concepts. Recent studies indicate that such imbalances can lead to suboptimal …
concepts. Recent studies indicate that such imbalances can lead to suboptimal …