A dimensionality reduction technique based on the Gromov-Wasserstein distance

TL;DR

This paper introduces a novel dimensionality reduction method leveraging the Gromov-Wasserstein distance, offering a probabilistic perspective and improved embedding of high-dimensional data into lower-dimensional spaces.

Contribution

It extends classical MDS and Isomap algorithms by incorporating the Gromov-Wasserstein distance, providing a new probabilistic framework for nonlinear dimensionality reduction.

Findings

Provides a robust embedding of high-dimensional data

Offers an efficient gradient descent-based optimization

Enhances classical DR algorithms with Gromov-Wasserstein distance

Abstract

Analyzing relationships between objects is a pivotal problem within data science. In this context, Dimensionality reduction (DR) techniques are employed to generate smaller and more manageable data representations. This paper proposes a new method for dimensionality reduction, based on optimal transportation theory and the Gromov-Wasserstein distance. We offer a new probabilistic view of the classical Multidimensional Scaling (MDS) algorithm and the nonlinear dimensionality reduction algorithm, Isomap (Isometric Mapping or Isometric Feature Mapping) that extends the classical MDS, in which we use the Gromov-Wasserstein distance between the probability measure of high-dimensional data, and its low-dimensional representation. Through gradient descent, our method embeds high-dimensional data into a lower-dimensional space, providing a robust and efficient solution for analyzing complex high-dimensional datasets.