Dimensionality reduction is often used for visu-alization without considering their understanding by users. Multidimensional scaling, for instance, provides an arbitrarily-oriented visualization. However, users can be integrated into the loop to provide clues about their understanding of the visualization. In this paper, we propose an interactive proba-bilistic multidimensional scaling (iPMDS) approach to compute the visualization with the lowest information loss while taking the information provided by users into account. We show that a more interpretable visualization can be obtained after interacting with the visualization while keeping a good dimensionality reduction accuracy.
|Title of host publication||International Joint Conference on Neural Networks|
|Number of pages||8|
|Publication status||Published - 2021|
- dimensionality reduction
- multidimensional sclaing (MDS)
- Probalistic Model
- User interaction