EntroPath defines a free-energy dissimilarity from maximum-entropy random walk path ensembles and proves it converges to squared geodesic distance in the short-time limit via Varadhan's formula.
Local multidimensional scaling.Neural Networks, 19(6):889–899, 2006
4 Pith papers cite this work, alongside 235 external citations. Polarity classification is still indexing.
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Pith papers citing it
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MEDAL distills manifold embeddings into autoencoders to enable out-of-sample extension and held-out validation of dimension reduction methods.
A spectral framework for nonlinear DR uses spectral bases plus cross-entropy optimization to create multi-scale embeddings that preserve both global manifold geometry and local neighborhoods while supporting graph-frequency analysis.
MAPLE enhances UMAP via self-supervised MMCRs to untangle complex manifolds, yielding clearer clusters and finer subclusters than standard UMAP at similar cost.
citing papers explorer
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EntroPath: Maximum Entropy Path Ensemble Embedding for Manifold Learning
EntroPath defines a free-energy dissimilarity from maximum-entropy random walk path ensembles and proves it converges to squared geodesic distance in the short-time limit via Varadhan's formula.
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MEDAL: Manifold Embedding Distillation via Autoencoder Learning
MEDAL distills manifold embeddings into autoencoders to enable out-of-sample extension and held-out validation of dimension reduction methods.
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A Spectral Framework for Multi-Scale Nonlinear Dimensionality Reduction
A spectral framework for nonlinear DR uses spectral bases plus cross-entropy optimization to create multi-scale embeddings that preserve both global manifold geometry and local neighborhoods while supporting graph-frequency analysis.
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MAPLE: Self-Supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis
MAPLE enhances UMAP via self-supervised MMCRs to untangle complex manifolds, yielding clearer clusters and finer subclusters than standard UMAP at similar cost.