5 papers
Gromov-Wasserstein Distillation for Inductive Multi-View Embedding
Rafael Pereira Eufrazio, Eduardo Fernandes Montesuma, Charles Casimiro Cavalcante
Gromov-Wasserstein multidimensional scaling (GW-MDS) learns low-dimensional representations from relational data but remains transductive, providing no explicit mapping for unseen…
Multi-Domain Clustering via Measure Quantization
Rafael Pereira Eufrazio, Eduardo Fernandes Montesuma, Charles Casimiro Cavalcante
Clustering is a fundamental task in data analysis, typically addressed through centroid-based methods such as K-means. In this work, we present a general framework for multi-domain…
Gromov-Wasserstein Methods for Multi-View Relational Embedding and Clustering
Rafael Pereira Eufrazio, Eduardo Fernandes Montesuma, Charles Casimiro Cavalcante
Learning low-dimensional representations from multi-view relational data is challenging when underlying geometries differ across views. We propose Bary-GWMDS, a Gromov-Wasserstein-…
Structure-Preserving Multi-View Embedding Using Gromov-Wasserstein Optimal Transport
Rafael Pereira Eufrazio, Eduardo Fernandes Montesuma, Charles Casimiro Cavalcante
Multi-view data analysis seeks to integrate multiple representations of the same samples in order to recover a coherent low-dimensional structure. Classical approaches often rely o…
A dimensionality reduction technique based on the Gromov-Wasserstein distance
Rafael P. Eufrazio, Eduardo Fernandes Montesuma, Charles C. Cavalcante
Analyzing relationships between objects is a pivotal problem within data science. In this context, Dimensionality reduction (DR) techniques are employed to generate smaller and mor…