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
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)
The paper introduces View distance, a novel metric that projects high‑dimensional data onto all pairwise two‑dimensional planes and sums the Euclidean distances across these projections. It satisfies metric axioms, couples features, suppresses redundancy, and captures anisotropic geometry. To make it scalable, the authors propose a plane‑selection strategy using iterative Maximum Weight Matching, reducing complexity from ω(n²) to ω(k) and demonstrating competitive performance on twelve datasets.
By Yiqun Zhang, Hou-biao Li
The paper introduces a unified bundle adjustment framework that jointly optimizes camera parameters, sparse 3D points, and richer geometric features such as lines, coplanarity, and parallelism. It classifies features into scalable ones with direct 2D measurements and higher‑order groups that can be treated as camera‑like entities, allowing group constraints and cross‑feature relations to be expressed via 2D reprojection errors. This approach preserves the sparsity of classical point‑based BA, maintains numerical stability, and achieves runtime comparable to point‑only BA while producing richer 3D structures and improved accuracy.
By Shaohui Liu, R\'emi Pautrat, Daniel Barath, Richard Hartley, Viktor Larsson, Marc Pollefeys
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
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