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

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.

Accès au document

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

Autres fichiers et liens

Lien vers la publication sur Scopus

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. 2020
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

Résumé

Une conférence

Financement

Accès au document

Autres fichiers et liens

Empreinte digitale

Thèses de l'étudiant

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

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