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cs.LG2026
A Spectral Decomposition Framework for Multiscale Nonlinear Dimensionality Reduction
Zeyang Huang, Angelos Chatzimparmpas, Thomas Höllt +1
Dimensionality reduction (DR) involves two longstanding trade-offs. First, preserving local neighborhoods can come at the cost of global structure. Neighbor embedding methods such…
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…