arXiv Machine Learning

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.

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

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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

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arXiv AI

Jul 14

Toward a Scientific Discovery Engine for Weather and Climate Data: A Visual Analytics Workbench for Embedding-Based Exploration

arXiv:2605. 00972v2 Announce Type: replace-cross Abstract: Earth system science is producing increasingly large, high-dimensional datasets from both physics-based and AI-driven models.

By Nihanth W. Cherukuru, Matt Rehme, Kirsten J. Mayer, David John Gagne, John Schreck, John Clyne, Charlie Becker

rag diffusion safety

arXiv AI

Jul 10

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.

By Duen Horng Chau, Donghao Ren, Fred Hohman, Dominik Moritz

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arXiv Machine Learning

Jul 1

Visualizing High-Dimensional Graph Embeddings via Informed Multi-View Projections

arXiv:2606. 31119v1 Announce Type: new Abstract: Graphs are commonly visualized in 2D, where humans readily interpret spatial relationships, yet such layouts often distort higher-dimensional structure.

By Ya Ji (Khoury College of Computer Sciences, Northeastern University, Seattle), Xuefeng Li (Khoury College of Computer Sciences, Northeastern University, Seattle), Timo Brand (School of Computation, Information and Technology, Technical University of Munich, Heilbronn, Germany), Jacob Miller (School of Computation, Information and Technology, Technical University of Munich, Heilbronn, Germany), Peng Zhang (Khoury College of Computer Sciences, Northeastern University, Seattle), Stephen Kobourov (School of Computation, Information and Technology, Technical University of Munich, Heilbronn, Germany), Yifan Hu (Khoury College of Computer Sciences, Northeastern University, Seattle)

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arXiv Machine Learning

Jul 17

cGAP: Generalized Association Plots with HOMALS-Guided Heatmaps for Visualization of High-Dimensional Categorical Data

arXiv:2607. 15018v1 Announce Type: cross Abstract: High-dimensional categorical data arise in genetics, biomedicine, and the social sciences, yet visualization tools for such data remain far less developed than those for continuous variables.

By Chun-houh Chen, Shun-Chuan Chang, Chiun-How Kao, Yi-Ju Lee, Shang-Ying Shiu, Yin-Jing Tien, ShengLi Tzeng, Han-Ming Wu

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arXiv Machine Learning

6d ago

CosMAP: Contrastive Manifold Approximation and Projection for Dimensionality Reduction of Omics and Genealogical Data

arXiv:2608. 11269v1 Announce Type: cross Abstract: Omics datasets, particularly single-cell RNA sequencing data, are high-dimensional, sparse, noisy, and dominated by zero values, making faithful low-dimensional representation challenging.

By Fenosoa Randrianjatovo, Maya Saleh, Simon Girard, Amadou Barry

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arXiv Machine Learning

Jul 27

LatentFlow: Visual Analytics for Latent Space Analysis in Molecular Graph Neural Networks

arXiv:2607. 21941v1 Announce Type: new Abstract: Chemists and materials scientists increasingly use machine learning models, such as graph neural networks (GNNs), to predict properties of molecules and the outcomes of their reactions.

By Shiyi Liu, Jiaqing Chen, Nicholas Hadler, Rostyslav Hnatyshyn, Michael W. Mahoney, Talita Perciano, John F. Hartwig, Gunther H. Weber, Ross Maciejewski

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arXiv Machine Learning

Jul 21

Stop Misusing t-SNE and UMAP for Visual Analytics

arXiv:2506. 08725v3 Announce Type: replace-cross Abstract: Misuses of t-SNE and UMAP in visual analytics have become increasingly common.

By Hyeon Jeon, Jeongin Park, Sungbok Shin, Jinwook Seo

arXiv Machine Learning

Jul 28

Queryable Self-Organizing Maps: A Database Abstraction for Topology-Driven Data Exploration

arXiv:2607. 22843v1 Announce Type: cross Abstract: Self-Organizing Maps (SOMs) have long been used as exploratory tools for high-dimensional data: they organize objects into a two-dimensional topology that reveals clusters, gradients, sparse regions, dense regions, and boundaries.

By Denis Mayr Lima Martins, Gottfried Vossen

data-engineering

arXiv AI

Jul 7

Scalable Semantic Steering of Embedding Projections

arXiv:2607. 03978v1 Announce Type: cross Abstract: Low-dimensional projections support interactive visual analysis of high-dimensional data embeddings, but their structure often does not align with analyst-defined semantic relationships.

By Wei Liu, Eric Krokos, Kirsten Whitley, Rebecca Faust, Chris North

llms rag multimodal safety

arXiv AI

Aug 3

Dimensionality reduction for homological stability and global structure preservation

arXiv:2503. 03156v4 Announce Type: replace-cross Abstract: We propose DiRe, a force-directed dimensionality reduction framework designed to preserve global structure and homological features while remaining practical on modern hardware.

By Alexander Kolpakov, Igor Rivin

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