Work

Do not get lost in projection: finding the right distance for meaningful UMAP embeddings

Year: 2025

Type: conference-paper

Abstract: Dimensionality reduction techniques are essential for visualizing and analyzing high-dimensional data.This study explores the impact of distance measures on the performance of Uniform Manifold Approximation and Projection (UMAP), a widely used dimensionality reduction method.We evaluate their influence on cluster separation, structure preservation, and their effectiveness when used as a preprocessing step for classification tasks on real and synthetic datasets.The results highlight the importance of tailoring distance measures to specific data contexts and provide guidance for optimizing UMAP applications.

Source: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

DOI: 10.14428/esann/2025.es2025-177

Authors: Eva Blanco-Mallo, Verónica Bolón-Canedo, Beatriz Remeseiro

Institutions: Universidade da Coruña, Universidad de Oviedo

Language: English

Topic: Face and Expression Recognition

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funders: Xunta de Galicia, Agencia Estatal de Investigación