AI RESEARCH
A Spectral Framework for Multi-Scale Nonlinear Dimensionality Reduction
arXiv CS.LG
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ArXi:2604.02535v1 Announce Type: new Dimensionality reduction (DR) is characterized by two longstanding trade-offs. First, there is a global-local preservation tension: methods such as t-SNE and UMAP prioritize local neighborhood preservation, yet may distort global manifold structure, while methods such as Laplacian Eigenmaps preserve global geometry but often yield limited local separation.