Learning Interpretability for Visualizations using Adapted Cox Models through a User Experiment. (arXiv:1611.06175v1 [stat.ML])

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.

from cs.AI updates on arXiv.org http://ift.tt/2eVjTZk
via IFTTT