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

Adrien Bibal, Rebecca Marion, Benoît Frenay

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Résumé

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.
langue originaleAnglais
titreESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Lieu de publicationBruges
Pages537-542
Nombre de pages6
ISBN (Electronique)9782875870476
Etat de la publicationPublié - 1 janv. 2018
Evénement 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018) - Bruges, Bruges, Belgique
Durée: 25 avr. 201827 avr. 2018

Série de publications

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

Une conférence

Une conférence 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018)
PaysBelgique
La villeBruges
période25/04/1827/04/18

Contient cette citation

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