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.
@article{arxiv.1611.06175,
title = {Learning Interpretability for Visualizations using Adapted Cox Models through a User Experiment},
author = {Adrien Bibal and Benoit Frénay},
journal= {arXiv preprint arXiv:1611.06175},
year = {2016}
}
Comments
Presented at NIPS 2016 Workshop on Interpretable Machine Learning in Complex Systems