Search Results for author:
Found 4 papers, 0 papers with code
Distributional Reduction: Unifying Dimensionality Reduction and Clustering with Gromov-Wasserstein Projection
Unsupervised learning aims to capture the underlying structure of potentially large and high-dimensional datasets.
Interpolating between Clustering and Dimensionality Reduction with Gromov-Wasserstein
We present a versatile adaptation of existing dimensionality reduction (DR) objectives, enabling the simultaneous reduction of both sample and feature sizes.
Optimal Transport with Adaptive Regularisation
Regularising the primal formulation of optimal transport (OT) with a strictly convex term leads to enhanced numerical complexity and a denser transport plan.
A Probabilistic Graph Coupling View of Dimension Reduction
Most popular dimension reduction (DR) methods like t-SNE and UMAP are based on minimizing a cost between input and latent pairwise similarities.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
