3 papers
stat.ML2025
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…
cs.GR2024
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…
cs.HC2024
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…