AI RESEARCH
DiRe-RAPIDS: Topology-faithful dimensionality reduction at scale
arXiv CS.LG
•
ArXi:2604.25209v1 Announce Type: new Dimensionality reduction methods such as UMAP and t-SNE are central tools for visualising high-dimensional data, but their local-neighborhood objectives can preserve sampling noise while distorting global topology. We show that standard local metrics reward this noise memorisation: top-performing embeddings invent cycles and disconnected islands absent from the data. We