- Walking Wales: the visualisation challenge
- Word Clouds: Can they be misleading?
- VIPS-Visual Interactive Parameter Steering
- Confirmation bias : do multiple views really help?
- Visual Query Interface for Infrastructure Networks
- MADucator – Matrix Educator
- Concensus Matrix Sort
- Multiscale Multiples: An Overview+Detail Interface for Small Multiples
- Analyzing Image Similarity to Detect Disguised Plagiarism
- Tackling the Multi-Analyst Problem in Soccer: How to Improve Collaborative Pattern Detection in Team Sports
- MDSQ – Quality Assessment of Distance-Preserving Projections
- Internal Quality Measures for Subspace Clusterings
- Exploration of Datasets for Visualizations of High-Dimensional Data
- Search and Visual Exploration of Scientific Literature
- Star Glyph – Optimal Dimension Layout
- Visual Analytics of Molecular Neurochemicals and Biological Markers in Mental Illness
- Ensembles of classifiers: Visual construction of classification models
- Cutting-Down the Complexity of Parallel Coordinates Plots in High-Dimensional Data
- Investigating Analytic Behavior in VA
- Hierarchical Matrix Visualization
- Realisierung und Evaluierung einer stereoskopischen 3D Perspective Wall Umgebung für verlinkte Informationsvisualisierung (Master)
- Combination of Matrices + Graphs
- TreeMap Evaluation
- Visual Analysis of Language Change over Time
- Visual Parameter Space Analysis of Topic Models
Overview and Background
Distance-preserving projections such as multi-dimensional scaling (MDS) or t-distributed stochastic neighborhood embedding (t-SNE) are popular methods to analyze high-dimensional data. The general idea is to project original data into a 2D layout by preserving the original similarities as well as possible. The analysis assumption is that close objects in the 2D projection correspond to similar objects in the high-dimensional space.
The visual patterns in 2D projections are often interpreted without questioning the quality of the projection. MDS, for example, optimizes the 2D layout by preserving all pair-wise similarities of the original data. Depending on the (dis-)similarity distribution, the MDS projection can reflect the original structures or not. The goal of this project is to develop methods for the quality assessment of such projections.
- Literature review for existing quality measures for projections.
- Development of quality criteria.
- Development of novel quality measures for distance-preserving projections.
- Development of a visual-interactive tool for the quality assessment of distance-preserving projections.
- Knowledge in information visualization.
- Scope: Bachelor/Master
- 6 Month Project, 3 Month Thesis (Bachelor) / 6 Month Thesis (Master)
- Start: immediately
|Theoretical (Analytical):||(5 / 5)|
|Practical (Implementation):||(5 / 5)|
|Literature Work:||(4 / 5)|
- Probing Projections: Interaction Techniques for Interpreting Arrangements and Errors of Dimensionality Reductions [Stahnke et al., 2016]
- ProxiViz: an Interactive Visualization Technique to Overcome Multidimensional Scaling Artifacts [Heulot et al., 2012]
- IPCA: An interactive system for PCA-based visual analytics [Jeong et al., 2009]
- Data-driven Evaluation of Visual Quality Measures [Sedlmair and Aupetit, 2015]
- Data Visualization With Multidimensional Scaling [Buja et al., 2008]
- Modern multidimensional scaling [Borg and Groenen, 2005]
- Visualizing Data using t-SNE [Maaten and Hinton, 2008]