User-steering Interpretable Visualization with Probabilistic Principal Components Analysis

Viet Minh Vu; Benoît Frénay

User-steering Interpretable Visualization with Probabilistic Principal Components Analysis

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

36 Téléchargements (Pure)

Résumé

The lack of interpretability generally in machine learning and specifically in visualization is often encountered.
Integration of user’s feedbacks into visualization process is a potential solution.
This paper shows that the user’s knowledge expressed by the positions of fixed points in the visualization can be transferred directly into a probabilistic principal components analysis (PPCA) model to help user steer the visualization.
Our proposed interactive PPCA model is evaluated with different datasets to prove the feasibility of creating explainable axes for the visualization.

langue originale	Anglais
titre	ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Sous-titre	27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Editeur	i6doc.com publication
Pages	349-354
Nombre de pages	6
ISBN (Electronique)	9782875870650
ISBN (imprimé)	978-287-587-065-0
Etat de la publication	Publié - 28 mars 2019
Evénement	27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Bruges, Belgique Durée: 24 avr. 2019 → 26 avr. 2019 https://www.elen.ucl.ac.be/esann/

Série de publications

Nom	ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Une conférence

Une conférence	27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Titre abrégé	ESANN 2019
Pays/Territoire	Belgique
La ville	Bruges
période	24/04/19 → 26/04/19
Adresse Internet	https://www.elen.ucl.ac.be/esann/

Accès au document

es2019-44Version finale publiée, 2,56 MB

https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2019-44.pdf

Autres fichiers et liens

Link to publication in Scopus

Contient cette citation

Vu, V. M., & Frénay, B. (2019). User-steering Interpretable Visualization with Probabilistic Principal Components Analysis. Dans ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (p. 349-354). (ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning). i6doc.com publication. https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2019-44.pdf

Vu, Viet Minh ; Frénay, Benoît. / User-steering Interpretable Visualization with Probabilistic Principal Components Analysis. ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. i6doc.com publication, 2019. p. 349-354 (ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

@inproceedings{26ebc5253043454fa0d31458ce0207f4,

title = "User-steering Interpretable Visualization with Probabilistic Principal Components Analysis",

abstract = "The lack of interpretability generally in machine learning and specifically in visualization is often encountered.Integration of user{\textquoteright}s feedbacks into visualization process is a potential solution.This paper shows that the user{\textquoteright}s knowledge expressed by the positions of fixed points in the visualization can be transferred directly into a probabilistic principal components analysis (PPCA) model to help user steer the visualization.Our proposed interactive PPCA model is evaluated with different datasets to prove the feasibility of creating explainable axes for the visualization.",

keywords = "machine learning, visualization, PCA, interpretation, probabilistic model",

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

note = "Funding Information: ∗Viet Minh Vu is supported by a UNamur-CERUNA institutional PhD grant. Publisher Copyright: {\textcopyright} 2019 ESANN (i6doc.com). All rights reserved.; 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2019 ; Conference date: 24-04-2019 Through 26-04-2019",

year = "2019",

month = mar,

day = "28",

language = "English",

isbn = "978-287-587-065-0",

series = "ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning",

publisher = "i6doc.com publication",

pages = "349--354",

booktitle = "ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning",

url = "https://www.elen.ucl.ac.be/esann/",

}

Vu, VM & Frénay, B 2019, User-steering Interpretable Visualization with Probabilistic Principal Components Analysis. Dans ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, i6doc.com publication, p. 349-354, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgique, 24/04/19. <https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2019-44.pdf>

User-steering Interpretable Visualization with Probabilistic Principal Components Analysis. / Vu, Viet Minh ; Frénay, Benoît.
ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. i6doc.com publication, 2019. p. 349-354 (ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

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 - User-steering Interpretable Visualization with Probabilistic Principal Components Analysis

AU - Vu, Viet Minh

AU - Frénay, Benoît

PY - 2019/3/28

Y1 - 2019/3/28

N2 - The lack of interpretability generally in machine learning and specifically in visualization is often encountered.Integration of user’s feedbacks into visualization process is a potential solution.This paper shows that the user’s knowledge expressed by the positions of fixed points in the visualization can be transferred directly into a probabilistic principal components analysis (PPCA) model to help user steer the visualization.Our proposed interactive PPCA model is evaluated with different datasets to prove the feasibility of creating explainable axes for the visualization.

AB - The lack of interpretability generally in machine learning and specifically in visualization is often encountered.Integration of user’s feedbacks into visualization process is a potential solution.This paper shows that the user’s knowledge expressed by the positions of fixed points in the visualization can be transferred directly into a probabilistic principal components analysis (PPCA) model to help user steer the visualization.Our proposed interactive PPCA model is evaluated with different datasets to prove the feasibility of creating explainable axes for the visualization.

KW - machine learning

KW - visualization

KW - PCA

KW - interpretation

KW - probabilistic model

UR - http://www.scopus.com/inward/record.url?scp=85071284821&partnerID=8YFLogxK

M3 - Conference contribution

SN - 978-287-587-065-0

T3 - ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

SP - 349

EP - 354

BT - ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

PB - i6doc.com publication

T2 - 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Y2 - 24 April 2019 through 26 April 2019

ER -

Vu VM , Frénay B. User-steering Interpretable Visualization with Probabilistic Principal Components Analysis. Dans ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. i6doc.com publication. 2019. p. 349-354. (ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

User-steering Interpretable Visualization with Probabilistic Principal Components Analysis

Résumé

Série de publications

Une conférence

Accès au document

Autres fichiers et liens

Empreinte digitale

Contient cette citation