arXiv AI July 10, 2026 By Duen Horng Chau, Donghao Ren, Fred Hohman, Dominik Moritz

Dimensionality Reduction Meets Network Science: Sensemaking on UMAP's kNN Graph

arXiv:2607. 08746v1 Announce Type: cross Abstract: While UMAP is widely used for exploring high-dimensional data, typical workflows focus on its lower-dimensional embedding, largely overlooking the rich k-nearest-neighbor (kNN) graph that UMAP constructs internally.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Jun 4

On Out-of-sample Embedding in UMAP

arXiv:2606. 04451v1 Announce Type: new Abstract: Neighbor embedding algorithms reveal correlations in high-dimensional data by constructing an equivalent graph representation in a lower-dimensional space.

By Mohammad Tariqul Islam, Jason W. Fleischer

rag

arXiv Machine Learning

Jul 13

Cluster and then Embed: A Modular Approach for Visualization

arXiv:2509. 03373v2 Announce Type: replace Abstract: Dimensionality reduction methods such as t-SNE and UMAP are popular methods for visualizing data with a potential (latent) clustered structure.

By Elizabeth Coda, Ery Arias-Castro, Gal Mishne

rag

arXiv Machine Learning

Jul 30

When One Point Is Not Enough: Addressing Ambiguous Instances in Dimensionality Reduction by Splitting

arXiv:2605. 23540v2 Announce Type: replace Abstract: Dimensionality Reduction (DR) methods are widely used to visualize high-dimensional data.

By Diede P. M. van der Hoorn, Alessio Arleo, Fernando V. Paulovich

rag

arXiv AI

Aug 10

Density-aware Hierarchical Clustering Based on Element-Categorized Connection Subgraphs

arXiv:2608. 06990v1 Announce Type: cross Abstract: Clustering is a fundamental data mining technique for pattern recognition through unsupervised learning.

By Yuning Yu, Jos\'e Rodr\'iguez-Pi\~neiro, Xuefeng Yin, Bin Feng

benchmarks

arXiv Machine Learning

Sep 16

A Spectral Decomposition Framework for Multiscale Nonlinear Dimensionality Reduction

arXiv:2604.02535v2 Announce Type: replace Abstract: Dimensionality reduction (DR) involves two longstanding trade-offs. First, preserving local neighborhoods can come at the cost of global structure....

By Zeyang Huang, Angelos Chatzimparmpas, Thomas H\"ollt, Takanori Fujiwara

rag

arXiv Statistics ML

3d ago

Barycentric subspace analysis of network-valued data

arXiv:2507.23559v2 Announce Type: replace-cross Abstract: Certain data are naturally modeled by networks or weighted graphs, be they biological networks or mobility networks. When there is no canonic...

By Elodie Maignant, Xavier Pennec, Alain Trouv\'e, Anna Calissano

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On Out-of-sample Embedding in UMAP

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