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cs.LG2026
MAPLE: Self-Supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis
Zeyang Huang, Takanori Fujiwara, Angelos Chatzimparmpas +2
We present a new nonlinear dimensionality reduction method, MAPLE, that enhances UMAP by improving manifold modeling. MAPLE employs a self-supervised learning approach to more effi…
cs.LG2026
A Spectral Framework for Multi-Scale Nonlinear Dimensionality Reduction
Zeyang Huang, Angelos Chatzimparmpas, Thomas Höllt +1
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…