190 citations · 634 across the 21 of their papers we have counts for
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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…
Bridging the gap between Performance and Interpretability: An Explainable Disentangled Multimodal Framework for Cancer Survival Prediction
Aniek Eijpe, Soufyan Lakbir, Melis Erdal Cesur +4
While multimodal survival prediction models are increasingly more accurate, their complexity often reduces interpretability, limiting insight into how different data sources influe…
LangLasso: Interactive Cluster Descriptions through LLM Explanation
Raphael Buchmüller, Dennis Collaris, Linhao Meng +1
Dimensionality reduction is a powerful technique for revealing structure and potential clusters in data. However, as the axes are complex, non-linear combinations of features, they…
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…