Neighbour Embeddings: Beyond Visualisation
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Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Machine learning (ML) has brought powerful tools to the visualisation community, particularly through neighbour embeddings (NE). This family of algorithms enables the intuitive visualisation of high dimensional datasets, by representing these in 2- or 3-dimensional spaces. This paper argues that as NE algorithms have progressed and diversified within the visualisation domain, they have matured into powerful yet often simple methods whose potential remains largely underutilised in broader machine learning contexts. This argument is illustrated by showing through two use cases, clustering and data preprocessing before a supervised task, how NE can contribute meaningfully with little additional algorithmic effort.
Description
@inproceedings{10.2312:mlvis.20251156,
booktitle = {Machine Learning Methods in Visualisation for Big Data},
editor = {Archambault, Daniel and Nabney, Ian and Peltonen, Jaakko},
title = {{Neighbour Embeddings: Beyond Visualisation}},
author = {Lambert, Pierre and Couplet, Edouard and Verleysen, Michel and Lee, John Aldo},
year = {2025},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-288-2},
DOI = {10.2312/mlvis.20251156}
}