Machine Learning Methods in Visualisation for Big Data 2018
Tutorial co-located with EuroVis 2018, June 4, 2018, Brno, Czech Republic
- 9:05-9:15 Introduction
- 9:15-9:45 Dimensionality Reduction for Visualization (Presenter: Jaakko Peltonen)
- Component analysis methods
- Self-organizing maps
- Multidimensional Scaling (MDS) and its variants
- Methods that preserve similarities or neighbourhood relationships
- 9:45-10:15 Sampling Methods and Graph Visualisation (Presenter: Daniel Archambault)
- Influence of sampling on perception of graph structure
- Sampling across time on dynamic graphs.
- 10:15-10:40 Paper presentation: “Panning for Insight: Amplifying Insight through Tight Integration of Machine Learning, Data Mining, and Visualization”, Benjamin Karer, Inga Scheler and Hans Hagen, TU Kaiserslautern, Germany
Slides in PDF format (note: full paper available as part of the Eurovis 2018 co-located event proceedings)
- 10:40-11:10 Coffee Break
- 11:10-11:45 Evaluating Visualisation Techniques (Presenter: Ian Nabney)
- Why is evaluation important yet difficult?
- User-based evaluation: perceptual evaluation, study design
- Metric-based evaluation: model-based metrics, unsupervised learning metrics, task-based metrics
- 11:45-12:15 Panel Discussion
- Future fruitful lines of research
- Obstacles/challenges to increased combination of ML and vis
- How to evaluate ML&vis at different levels, and convince others of the evaluation?
- How do we relate do data science?
- Topics for the next CFP
- 12:15-12:45 Data Lab: Bring Your Own Data
- 12:45-12:50 Closing