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

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Résumé

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.
langue originaleAnglais
titreNIPS Workshop on Interpretable Machine Learning in Complex Systems
Lieu de publicationBarcelona
Etat de la publicationPublié - 2016
EvénementThirtieth Conference on Neural Information Processing Systems - Sagrada Familia, Barcelonne, Espagne
Durée: 5 déc. 201610 déc. 2016

Colloque

ColloqueThirtieth Conference on Neural Information Processing Systems
Titre abrégéNIPS 2016 (NeurIPS)
PaysEspagne
La villeBarcelonne
période5/12/1610/12/16

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