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
titre 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Sous-titreESANN 2018 : Bruges, Belgium, April 25, 26, 27, 2018
rédacteurs en chefMichel Verleysen
Lieu de publicationLouvain-la-Neuve
EditeurCIACO
Pages537-542
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

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

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