AI RESEARCH
FastUMAP: Scalable Dimensionality Reduction via Bipartite Landmark Sampling
arXiv CS.LG
•
ArXi:2605.11428v1 Announce Type: new Exploratory analysis of high-dimensional data rarely stops at a single embedding. In practice, analysts rerun dimensionality reduction after changing preprocessing, subsets, or hyperparameters, and standard nonlinear methods can quickly become the bottleneck. We