From the 1 of 6 linked papers with an AI index.
6 papers
Measuring Distortion in the Empty Regions of Dimensionality Reduction Scatterplots with the Gap Index
Jaume Ros, Alessio Arleo, Fernando Paulovich
The paper proposes the Gap Index, a metric that measures how much empty regions in 2D dimensionality‑reduction scatterplots are distorted compared to the original high‑dimensional…
When One Point Is Not Enough: Addressing Ambiguous Instances in Dimensionality Reduction by Splitting
Diede P. M. van der Hoorn, Alessio Arleo, Fernando V. Paulovich
Dimensionality Reduction (DR) methods are widely used to visualize high-dimensional data. One key task in DR-based analysis is discovering neighborhoods, which relies on analyzing…
Mind the Gaps: Measuring Visual Artifacts in Dimensionality Reduction
Jaume Ros, Alessio Arleo, Fernando Paulovich
Dimensionality Reduction (DR) techniques are commonly used for the visual exploration and analysis of high-dimensional data due to their ability to project datasets of high-dimensi…
Why Can't I See My Clusters? A Precision-Recall Approach to Dimensionality Reduction Validation
Diede P. M. van der Hoorn, Alessio Arleo, Fernando V. Paulovich
Dimensionality Reduction (DR) is widely used for visualizing high-dimensional data, often with the goal of revealing expected cluster structure. However, such a structure may not a…
Reflections on the Use of Dashboards in the Covid-19 Pandemic
Alessio Arleo, Rita Borgo, Jörn Kohlhammer +3
Dashboards have arguably been the most used visualizations during the COVID-19 pandemic. They were used to communicate its evolution to national governments for disaster mitigation…
When Dimensionality Reduction Meets Graph (Drawing) Theory: Introducing a Common Framework, Challenges and Opportunities
Fernando Paulovich, Alessio Arleo, Stef van den Elzen
In the vast landscape of visualization research, Dimensionality Reduction (DR) and graph analysis are two popular subfields, often essential to most visual data analytics setups. D…