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1 to 5 of 5 Results
Replication Data for: Uncertainty-Aware Principal Component Analysis
Dec 7, 2022
Görtler, Jochen; Spinner, Thilo; Weiskopf, Daniel; Deussen, Oliver, 2022, "Replication Data for: Uncertainty-Aware Principal Component Analysis", https://doi.org/10.18419/darus-2321, DaRUS, V1
This dataset contains the source code for uncertainty-aware principal component analysis (UA-PCA) and a series of images that show dimensionality reduction plots created with UA-PCA. The software is a JavaScript library for performing principal component analysis and dimensionali...
Supplemental Material for Uncertainty-Aware Multidimensional Scaling
Oct 13, 2022
Hägele, David; Krake, Tim; Weiskopf, Daniel, 2022, "Supplemental Material for Uncertainty-Aware Multidimensional Scaling", https://doi.org/10.18419/darus-3104, DaRUS, V1
This dataset contains the supplemental material for "Uncertainty-Aware Multidimensional Scaling". Uncertainty-aware multidimensional scaling (UAMDS) is a nonlinear dimensionality reduction technique for sets of random vectors. This dataset consists of a PDF document that contains...
Uncertainty visualization: Fundamentals and recent developments, code to produce data and visuals used in Section 5
Oct 11, 2022 - SFB-TRR 161 B07 "Computational Uncertainty Quantification"
Beschle, Cedric; Barth, Andrea, 2022, "Uncertainty visualization: Fundamentals and recent developments, code to produce data and visuals used in Section 5", https://doi.org/10.18419/darus-3154, DaRUS, V1
Python code to generate the meshes and FEM solutions to Section 5 of the paper Uncertainty visualization: Fundamentals and recent developments. Comments are in the code to explain it. Paraview is used for the visualization.
Supplemental Material for Relaxed Dot Plots: Faithful Visualization of Samples and Their Distribution
Supplemental Material for Relaxed Dot Plots: Faithful Visualization of Samples and Their Distribution
Sep 21, 2022 - SFB-TRR 161 B01 "Adaptive Self-Consistent Visualization"
Rodrigues, Nils; Schulz, Christoph; Döring, Sören; Baumgartner, Daniel; Krake, Tim; Weiskopf, Daniel, 2022, "Supplemental Material for Relaxed Dot Plots: Faithful Visualization of Samples and Their Distribution", https://doi.org/10.18419/darus-3055, DaRUS, V1
Supplemental material for the paper "Relaxed Dot Plots: Faithful Visualization of Samples and Their Distribution". Contains: math behind Relaxed Dot Plots additional images pseudo-anonymous study data source code for library and test application To view the material, extract supp...
Source Code for Uncertainty-Aware Multidimensional Scaling
Aug 8, 2022
Hägele, David; Krake, Tim, 2022, "Source Code for Uncertainty-Aware Multidimensional Scaling", https://doi.org/10.18419/darus-2995, DaRUS, V1
This dataset contains the source code for the uncertainty-aware multidimensional scaling (UAMDS) algorithm implemented in the Java programming language. UAMDS is a nonlinear dimensionality reduction technique for sets of random vectors. The implemented UAMDS model allows to proje...
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