arXiv Machine Learning

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.

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

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 31

FADEx: Feature Attribution and Distortion-based Explanation of Dimensionality Reduction

arXiv:2607. 27463v1 Announce Type: new Abstract: Dimensionality Reduction (DR) is a fundamental tool for high-dimensional data exploration, reducing the complexity of latent spaces of machine learning models, and assisting in the explanation of complex opaque models.

By Lucas Greff Meneses, Evandro S. Ortigossa, Claudio Silva, Luis Gustavo Nonato

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

rag benchmarks

arXiv Machine Learning

Jul 31

Measuring Distortion in the Empty Regions of Dimensionality Reduction Scatterplots with the Gap Index

arXiv:2607. 28324v1 Announce Type: new Abstract: Quality metrics play a crucial role in the proper use of dimensionality reduction projections for visual analysis of high-dimensional data.

By Jaume Ros, Alessio Arleo, Fernando Paulovich

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

Jun 9

MS-COOT: Comparing Morse-Smale Complexes with Co-Optimal Transport

arXiv:2606. 08258v1 Announce Type: cross Abstract: Understanding and comparing structures in scalar fields is a central challenge in scientific visualization, with applications ranging from feature analysis to temporal and structural comparison.

By Guangyu Meng, Mingzhe Li, Erin Wolf Chambers

Hugging Face Trending Papers

Aug 5

HiSC: Hierarchical Spatial Clustering Token Compression for Efficient 3D Scene Understanding

3D vision-language models (3D VLMs) enable spatial reasoning over multi-view scenes but suffer from substantial token redundancy due to duplicated observations and large uninformative regions, leading to high computational cost. Although visual token compression has shown promise in accelerating 2D VLMs, it fails to capture the structured nature of 3D scenes and leads to incomplete spatial coverage and loss of fine-grained details.

llms efficiency multimodal benchmarks

arXiv AI

1d ago

MoE-Enhanced Explainable Deep Manifold Transformation for Complex Data Embedding and Visualization

arXiv:2410. 19504v3 Announce Type: replace-cross Abstract: Dimensionality reduction (DR) plays a crucial role in various fields, including data engineering and visualization, by simplifying complex datasets while retaining essential information.

By Zelin Zang, Yuhao Wang, Jinlin Wu, Hong Liu, Yue Shen, Zhen Lei, Stan Z. Li

rag data-engineering

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

rag safety

arXiv Machine Learning

Jul 30

FloDR: An invertible dimensionality reduction method based on a normalising flow

arXiv:2607. 26278v1 Announce Type: new Abstract: It is common for two-dimensional embeddings of high-dimensional data to be read far beyond what they can support.

By Abdallah Baraka, Daniel Probst

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