User-steering Interpretable Visualization with Probabilistic Principal Components Analysis

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Résumé

The lack of interpretability generally in machine learning and specifically in visualization is often encountered.
Integration of user’s feedbacks into visualization process is a potential solution.
This paper shows that the user’s knowledge expressed by the positions of fixed points in the visualization can be transferred directly into a probabilistic principal components analysis (PPCA) model to help user steer the visualization.
Our proposed interactive PPCA model is evaluated with different datasets to prove the feasibility of creating explainable axes for the visualization.
langue originaleAnglais
titreESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Sous-titre27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Editeuri6doc.com publication
Pages349-354
Nombre de pages6
ISBN (Electronique)9782875870650
ISBN (imprimé)978-287-587-065-0
Etat de la publicationPublié - 28 mars 2019
Evénement27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Bruges, Belgique
Durée: 24 avr. 201926 avr. 2019
https://www.elen.ucl.ac.be/esann/

Série de publications

NomESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Une conférence

Une conférence27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Titre abrégéESANN 2019
PaysBelgique
La villeBruges
période24/04/1926/04/19
Adresse Internet

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  • Contient cette citation

    Vu, V. M., & Frenay, B. (2019). User-steering Interpretable Visualization with Probabilistic Principal Components Analysis. Dans ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (p. 349-354). (ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning). i6doc.com publication. https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2019-44.pdf