Finding the most interpretable MDS rotation for sparse linear models based on external features

BIBAL, Adrien;Marion, Rebecca;Frénay, Benoît
(2018) European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning — Location: Bruges, Belgium (25.April.2018)

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  • BIBAL, AdrienUNamur
    Author
  • Marion, Rebeccaorcid-logoUCLouvain
    Author
  • Frénay, BenoîtUNamur
    Author
Abstract
One approach to interpreting multidimensional scaling (MDS) embeddings is to estimate a linear relationship between the MDS dimensions 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.
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BIBAL, A., Marion, R., & Frénay, B. (2018). Finding the most interpretable MDS rotation for sparse linear models based on external features. ESANN 2018 proceedings, p. 537-542. https://hdl.handle.net/2078.5/223008