2 citations · 3 across the 8 of their papers we have counts for
8 papers
Uncertainty-Aware PCA for Arbitrarily Distributed Data Modeled by Gaussian Mixture Models
Daniel Klötzl, Ozan Tastekin, David Hägele +2
Multidimensional data is often associated with uncertainties that are not well-described by normal distributions. In this work, we describe how such distributions can be projected…
Two Empirical Studies on Audiovisual Semiotics of Uncertainty
Sita Vriend, David Hägele, Daniel Weiskopf
There exists limited theoretical guidance on integrating visualization and sonification. In this paper, we address this gap by investigating audiovisual semiotics for uncertainty r…
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…
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…
Visualization of Nonlinear Programming for Robot Motion Planning
David Hägele, Moataz Abdelaal, Ozgur S. Oguz +2
Nonlinear programming targets nonlinear optimization with constraints, which is a generic yet complex methodology involving humans for problem modeling and algorithms for problem s…