4 papers
Dimensionality Reduction Considered Harmful (Some of the Time)
Hyeon Jeon
Visual analytics now plays a central role in decision-making across diverse disciplines, but it can be unreliable: the knowledge or insights derived from the analysis may not accur…
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…
Beyond the Mirror: Personal Analytics through Visual Juxtaposition with Other People's Data
Sungbok Shin, Sunghyo Chung, Hyeon Jeon +6
An individual's data can reveal facets of behavior and identity, but its interpretation is context dependent. We can easily identify various self-tracking applications that help pe…
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…