Distance-based dimensionality reduction for big dat

P. F. Delicado Useros, A. Casanova Lloveras, C. Pachón García

Dimensionality reduction (DR) involves projecting a high-dimensional dataset into a lower-dimensional space. Many DR techniques have been proposed; most of them are based on the inter-individual distance matrix. However, using full distance matrices becomes impractical when the number of individuals is very large due to significant computational time and memory requirements. Although there are algorithms that extend classical multidimensional scaling (MDS) to big data, many use specific elements of classical MDS and cannot be applied to other DR methods. One exception is the divide-and-conquer algorithm, which we adapt in this work for use with any generic, distance-based DR method. We implemented a generalized Python framework for distance-based DR methods that uses the divide-and-conquer strategy to reduce time and memory complexities. We tested our framework with non-classical MDS, local MDS, Isomap, and t-SNE.

Keywords: Divide-and-conquer, Procrustes transformations, non-classical MDS, local MDS, Isomap, t-SNE

GT AMyC I: Advances in Distance-Based Methods
September 4, 2026  9:00 AM
Aula 28

M. Sánchez Ronco, S. Olivera, A. Grané

F. Scielzo Ortiz, A. Grané, M. Diaz Gorfinkiel

M. Álvarez Martín, E. Boj del Val, A. Grané Chávez

S. Díez Martínez, A. Arribas Gil, A. Grané Chávez