Lambert, PierreCouplet, EdouardVerleysen, MichelLee, John AldoArchambault, DanielNabney, IanPeltonen, Jaakko2025-05-262025-05-262025978-3-03868-288-2https://doi.org/10.2312/mlvis.20251156https://diglib.eg.org/handle/10.2312/mlvis20251156Machine 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.Attribution 4.0 International License10.2312/mlvis.202511565 pages