3 papers
cs.LG2025
UMATO: Bridging Local and Global Structures for Reliable Visual Analytics with Dimensionality Reduction
Hyeon Jeon, Kwon Ko, Soohyun Lee +5
Due to the intrinsic complexity of high-dimensional (HD) data, dimensionality reduction (DR) techniques cannot preserve all the structural characteristics of the original data. The…
cs.HC2025
Navigating High-Dimensional Backstage: A Guide for Exploring Literature for the Reliable Use of Dimensionality Reduction
Hyeon Jeon, Hyunwook Lee, Yun-Hsin Kuo +6
Visual analytics using dimensionality reduction (DR) can easily be unreliable for various reasons, e.g., inherent distortions in representing the original data. The literature has…
cs.HC2025
Unveiling High-dimensional Backstage: A Survey for Reliable Visual Analytics with Dimensionality Reduction
Hyeon Jeon, Hyunwook Lee, Yun-Hsin Kuo +6
Dimensionality reduction (DR) techniques are essential for visually analyzing high-dimensional data. However, visual analytics using DR often face unreliability, stemming from fact…