User-steering Interpretable Visualization with Probabilistic Principal Components Analysis

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Abstract

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.
Original languageEnglish
Title of host publicationESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Subtitle of host publication27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Publisheri6doc.com publication
Pages349-354
Number of pages6
ISBN (Electronic)9782875870650
ISBN (Print)978-287-587-065-0
Publication statusPublished - 28 Mar 2019
Event27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Bruges, Belgium
Duration: 24 Apr 201926 Apr 2019
https://www.elen.ucl.ac.be/esann/

Publication series

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

Conference

Conference27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Abbreviated titleESANN 2019
CountryBelgium
CityBruges
Period24/04/1926/04/19
Internet address

Keywords

  • machine learning
  • visualization
  • PCA
  • interpretation
  • probabilistic model

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  • Cite this

    Vu, V. M., & Frenay, B. (2019). User-steering Interpretable Visualization with Probabilistic Principal Components Analysis. In 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 (pp. 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