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 originale | Anglais |
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titre | ESANN 2020 |
Sous-titre | 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning |
Lieu de publication | Bruges, Belgium |
Editeur | ESANN (i6doc.com) |
Pages | 393-398 |
ISBN (Electronique) | 978-287587074-2 |
ISBN (imprimé) | 9978-2-87587-073-5 |
Etat de la publication | Publié - 21 oct. 2020 |
Evénement | 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: ESANN2020 - Bruges, Belgique Durée: 2 oct. 2020 → 4 oct. 2020 |
Une conférence
Une conférence | 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning |
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Pays/Territoire | Belgique |
La ville | Bruges |
période | 2/10/20 → 4/10/20 |
Financement
∗The first three authors have contributed equally. G. Nanfack is funded by the EOS Ver-iLearn project n. 30992574 of the Fonds de la Recherche Scientifique (F.R.S-FNRS) in Belgium.
Empreinte digitale
Examiner les sujets de recherche de « Explaining t-SNE embeddings locally by adapting LIME ». Ensemble, ils forment une empreinte digitale unique.Thèses de l'étudiant
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Interpretability and Explainability in Machine Learning and their Application to Nonlinear Dimensionality Reduction
Bibal, A. (Auteur), FRENAY, B. (Promoteur), VANHOOF, W. (Président), Cleve, A. (Jury), Dumas, B. (Jury), Lee, J. A. (Jury) & Galarraga, L. (Jury), 16 nov. 2020Student thesis: Doc types › Docteur en Sciences
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