1 citations · 1 across the 9 of their papers we have counts for
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Progressive Glimmer: Expanding Dimensionality in Multidimensional Scaling
Marina Evers, David Hägele, Sören Döring +1
Progressive dimensionality reduction algorithms allow for visually investigating intermediate results, especially for large data sets. While different algorithms exist that progres…
Maximum entropy and quantized metric models for absolute category ratings
Dietmar Saupe, Krzysztof Rusek, David Hägele +2
The datasets of most image quality assessment studies contain ratings on a categorical scale with five levels, from bad (1) to excellent (5). For each stimulus, the number of ratin…
UADAPy: An Uncertainty-Aware Visualization and Analysis Toolbox
Patrick Paetzold, David Hägele, Marina Evers +2
Current research provides methods to communicate uncertainty and adapts classical algorithms of the visualization pipeline to take the uncertainty into account. Various existing vi…
Visual Compositional Data Analytics for Spatial Transcriptomics
David Hägele, Yuxuan Tang, Daniel Weiskopf
For the Bio+Med-Vis Challenge 2024, we propose a visual analytics system as a redesign for the scatter pie chart visualization of cell type proportions of spatial transcriptomics d…
Bridging Quantitative and Qualitative Methods for Visualization Research: A Data/Semantics Perspective in Light of Advanced AI
Daniel Weiskopf
This paper revisits the role of quantitative and qualitative methods in visualization research in the context of advancements in artificial intelligence (AI). The focus is on how w…
Out-of-Core Dimensionality Reduction for Large Data via Out-of-Sample Extensions
Luca Reichmann, David Hägele, Daniel Weiskopf
Dimensionality reduction (DR) is a well-established approach for the visualization of high-dimensional data sets. While DR methods are often applied to typical DR benchmark data se…