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
The paper introduces the Rashomon set for dimension reduction, a collection of equally good embeddings that preserve high‑dimensional structure. It proposes PCA‑informed alignment to make axes interpretable, concept‑alignment regularization to incorporate external knowledge, and a method to extract trustworthy nearest‑neighbor relationships across the Rashomon set for refined embeddings. These techniques aim to produce interpretable, robust, and goal‑aligned visualizations by leveraging multiple valid embeddings instead of a single one.
By Yiyang Sun, Haiyang Huang, Gaurav Rajesh Parikh, Cynthia Rudin
LatentVerse is a new framework that provides a web-based visual analytics platform and a command-line interface for analyzing multimodal latent representations. It unifies diagnostics for representation quality metrics and extends analysis to multimodal settings by decomposing embeddings into shared and modality-specific components. The authors evaluate the tool through simulations, real biomedical data analyses, and a user study, demonstrating its utility for interpretable evaluation of foundation model representations.
By Majd Alafrange, Samuel Friedman, John Kitonyo, Sana Tonekaboni, Mahnaz Maddah
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:2606. 00370v1 Announce Type: cross Abstract: Diverse genomics data, scientific questions, and analysis tasks typically demand highly specialized visualizations.
By Astrid van den Brandt, Kiroong Choe, Sehi L'Yi, Devin Lange, Nils Gehlenborg
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