Visualizations are powerful tools that are commonly used by data scientists to get more insights about their high dimensional data. One can for example cite t-SNE, which is probably one of the most famous and widely-used visualization techniques. However, t-SNE is a nonlinear and non-parametric technique that makes it suffer from a lack of interpretability. In this paper, we present a new technique inspired by t-SNE’s objective function that combines its ability to build nice visualizations with the interpretability of decision trees. This new visualization technique, called DT-SNE, can be seen as a discrete visualization technique where groups of instances are provided, as well as a ranking between them. The decision rules of the decision tree provide clear insights to interpret these different groups.
|Number of pages||12|
|Publication status||Published - 7 Apr 2023|
- Decision trees
- Nonlinear dimensionality reduction