# Learning Interpretability for Visualizations using Adapted Cox Models through a User Experiment

arXiv: https://arxiv.org/abs/1611.06175 · HTML: https://mlsanity.com/paper/1611.06175/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2016.

## Built on by (most cited first, 3 of 3)

- [Stop Misusing t-SNE and UMAP for Visual Analytics](https://mlsanity.com/paper/2506.08725/) (2025)
- [DumbleDR: Predicting User Preferences of Dimensionality Reduction Projection Quality](https://mlsanity.com/paper/2105.09275/) (2021)
- [Understanding Bias in Perceiving Dimensionality Reduction Projections](https://mlsanity.com/paper/2507.20805/) (2025)

## Abstract (arXiv)

In order to be useful, visualizations need to be interpretable. This paper uses a user-based approach to combine and assess quality measures in order to better model user preferences. Results show that cluster separability measures are outperformed by a neighborhood conservation measure, even though the former are usually considered as intuitively representative of user motives. Moreover, combining measures, as opposed to using a single measure, further improves prediction performances.