Explaining t-SNE embeddings locally by adapting LIME

Adrien Bibal; Viet Minh Vu; Geraldin Nanfack; Benoît Frénay

Explaining t-SNE embeddings locally by adapting LIME

Adrien Bibal, Viet Minh Vu, Geraldin Nanfack, Benoît Frénay

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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 originale	Anglais
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
Pays/Territoire	Belgique
La ville	Bruges
période	2/10/20 → 4/10/20

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2020-Bibal-Explaining t-SNE Embeddings Locally by Adapting LIMEmanuscrit soumis, 932 KB
2020_BibalAEtal_Article_ExplainingAccepted author manuscript, 932 KB

https://dblp.org/db/conf/esann/esann2020.html

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Interpretability and Explainability in Machine Learning and their Application to Nonlinear Dimensionality Reduction
Auteur: Bibal, A., 16 nov. 2020
Superviseur: FRENAY, B. (Promoteur), VANHOOF, W. (Président), Cleve, A. (Jury), Dumas, B. (Jury), Lee, J. A. (Personne externe) (Jury) & Galarraga, L. A. (Personne externe) (Jury)
Student thesis: Doc types › Docteur en Sciences

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Contient cette citation

@inproceedings{72df8a506a814971a75ced4054a0396f,

title = "Explaining t-SNE embeddings locally by adapting LIME",

abstract = "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.",

author = "Adrien Bibal and Vu, {Viet Minh} and Geraldin Nanfack and Beno{\^i}t Fr{\'e}nay",

note = "Funding Information: ∗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. Publisher Copyright: {\textcopyright} ESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.; 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning : ESANN2020 ; Conference date: 02-10-2020 Through 04-10-2020",

year = "2020",

month = oct,

day = "21",

language = "English",

isbn = "9978-2-87587-073-5",

pages = "393--398",

booktitle = "ESANN 2020",

publisher = "ESANN (i6doc.com)",

}

Bibal, A , Vu, VM , Nanfack, G & Frénay, B 2020, Explaining t-SNE embeddings locally by adapting LIME. Dans ESANN 2020 : 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. ESANN (i6doc.com), Bruges, Belgium, p. 393-398, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgique, 2/10/20. <https://dblp.org/db/conf/esann/esann2020.html>

Explaining t-SNE embeddings locally by adapting LIME. / Bibal, Adrien ; Vu, Viet Minh ; Nanfack, Geraldin et al.

ESANN 2020 : 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges, Belgium : ESANN (i6doc.com), 2020. p. 393-398.

Résultats de recherche: Contribution dans un livre/un catalogue/un rapport/dans les actes d'une conférence › Article dans les actes d'une conférence/un colloque

TY - GEN

T1 - Explaining t-SNE embeddings locally by adapting LIME

AU - Bibal, Adrien

AU - Vu, Viet Minh

AU - Nanfack, Geraldin

AU - Frénay, Benoît

N1 - Funding Information: ∗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. Publisher Copyright: © ESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.

PY - 2020/10/21

Y1 - 2020/10/21

N2 - 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.

AB - 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.

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M3 - Conference contribution

AN - SCOPUS:85098949059

SN - 9978-2-87587-073-5

SP - 393

EP - 398

BT - ESANN 2020

PB - ESANN (i6doc.com)

CY - Bruges, Belgium

T2 - 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Y2 - 2 October 2020 through 4 October 2020

ER -

Explaining t-SNE embeddings locally by adapting LIME

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Une conférence

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Thèses de l'étudiant

Interpretability and Explainability in Machine Learning and their Application to Nonlinear Dimensionality Reduction

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