Explaining t-SNE embeddings locally by adapting LIME

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Résumé

Non-linear dimensionality reduction techniques, such as tSNE, are widely used to visualize and analyze high-dimensional datasets. While non-linear projections can be of high quality, it is hard, or even impossible, to interpret the dimensions of the obtained embeddings. This paper adapts LIME to locally explain t-SNE embeddings. More precisely, the sampling and black-box-querying steps of LIME are modified so that they can be used to explain t-SNE locally. The result of the proposal is to provide, for a particular instance x and a particular t-SNE embedding Y, an interpretable model that locally explains the projection of x on Y.

langue originaleAnglais
titreESANN 2020
Sous-titre28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Lieu de publicationBruges, Belgium
EditeurESANN (i6doc.com)
Pages393-398
ISBN (Electronique)978-287587074-2
ISBN (imprimé)9978-2-87587-073-5
Etat de la publicationPublié - 21 oct. 2020
Evénement28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: ESANN2020 - Bruges, Belgique
Durée: 2 oct. 20204 oct. 2020

Une conférence

Une conférence28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
PaysBelgique
La villeBruges
période2/10/204/10/20

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