arXiv AI

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.

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 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

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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 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

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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 13

Scalable Varied-Density Clustering via Graph Propagation

arXiv:2508. 02989v2 Announce Type: replace Abstract: We propose a novel perspective on varied-density clustering for high-dimensional data by framing it as a label propagation process in neighborhood graphs that adapt to local density variations.

By Ninh Pham, Yingtao Zheng, Hugo Phibbs

arXiv Machine Learning

Jul 28

On a linear fused Gromov-Wasserstein distance for graph structured data

arXiv:2203. 04711v2 Announce Type: replace Abstract: We present a framework for embedding graph structured data into a vector space, taking into account node features and topology of a graph into the optimal transport (OT) problem.

By Dai Hai Nguyen, Koji Tsuda

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arXiv Machine Learning

Jun 19

Toward General Digraph Contrastive Learning: A Dual Spatial Perspective

arXiv:2510. 16311v3 Announce Type: replace Abstract: Graph Contrastive Learning (GCL) has emerged as a powerful tool for extracting consistent representations from graphs, independent of labeled information.

By Zhengyu Wu, Daohan Su, Yang Zhang, Xunkai Li, Rong-Hua Li, Guoren Wang

benchmarks

arXiv Machine Learning

Jul 2

FastUMAP: Scalable Dimensionality Reduction via Bipartite Landmark Sampling

arXiv:2605. 11428v2 Announce Type: replace Abstract: Exploratory analysis of high-dimensional data rarely stops at a single embedding.

By Hongmin Li

rag benchmarks

arXiv Machine Learning

1d ago

Efficient Coreset Selection via K-Nearest Neighbor Graphs

arXiv:2608. 16270v1 Announce Type: new Abstract: Coreset selection reduces the cost of model training by replacing a large training set with a small representative subset.

By Yingfan Liu, Leiyu Zhang, Jiadong Xie, Mingzhe Wang, Jeffrey Xu Yu, Jiangtao Cui

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 8

Intuitionistic Fuzzy Graph Embedded Random Vector Functional Link with Multiview Learning

arXiv:2607. 05635v1 Announce Type: new Abstract: Random Vector Functional Link (RVFL) networks are popular due to their fast training and universal approximation capabilities.

By Vrushank Ahire, Yogesh Kumar, M. A. Ganaie

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