Finding the Most Interpretable MDS Rotation for Sparse Linear Models based on External Features

Adrien Bibal, Rebecca Marion, Benoît Frenay

Research output: Contribution in Book/Catalog/Report/Conference proceedingConference contribution

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Abstract

One approach to interpreting multidimensional scaling (MDS)
embeddings is to estimate a linear relationship between the MDS dimen-
sions and a set of external features. However, because MDS only preserves
distances between instances, the MDS embedding is invariant to rotation.
As a result, the weights characterizing this linear relationship are arbitrary
and difficult to interpret. This paper proposes a procedure for selecting
the most pertinent rotation for interpreting a 2D MDS embedding.
Original languageEnglish
Title of host publicationESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Place of PublicationBruges
Pages537-542
Number of pages6
ISBN (Electronic)9782875870476
Publication statusPublished - 1 Jan 2018
Event 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018) - Bruges, Bruges, Belgium
Duration: 25 Apr 201827 Apr 2018

Publication series

NameESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Conference

Conference 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018)
CountryBelgium
CityBruges
Period25/04/1827/04/18

Keywords

  • Machine learning
  • Interpretability
  • Dimensionality reduction
  • Multidimensional scaling
  • Multi-view
  • Sparsity
  • Lasso regularization

Cite this

Bibal, A., Marion, R., & Frenay, B. (2018). Finding the Most Interpretable MDS Rotation for Sparse Linear Models based on External Features. In ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 537-542). (ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning)..