User-steering Interpretable Visualization with Probabilistic Principal Components Analysis

Viet Minh Vu; Benoît Frénay

User-steering Interpretable Visualization with Probabilistic Principal Components Analysis

Research output: Contribution in Book/Catalog/Report/Conference proceeding › Conference contribution

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Abstract

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.

Original language	English
Title of host publication	ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Subtitle of host publication	27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Publisher	i6doc.com publication
Pages	349-354
Number of pages	6
ISBN (Electronic)	9782875870650
ISBN (Print)	978-287-587-065-0
Publication status	Published - 28 Mar 2019
Event	27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Bruges, Belgium Duration: 24 Apr 2019 → 26 Apr 2019 https://www.elen.ucl.ac.be/esann/

Publication series

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

Conference

Conference	27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Abbreviated title	ESANN 2019
Country/Territory	Belgium
City	Bruges
Period	24/04/19 → 26/04/19
Internet address	https://www.elen.ucl.ac.be/esann/

Keywords

machine learning
visualization
PCA
interpretation
probabilistic model

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es2019-44Final published version, 2.56 MB

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

Cite this

Vu, V. M., & Frénay, B. (2019). User-steering Interpretable Visualization with Probabilistic Principal Components Analysis. In 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 (pp. 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. pp. 349-354 (ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

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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. in 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, pp. 349-354, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, 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).

Research output: Contribution in Book/Catalog/Report/Conference proceeding › Conference contribution

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

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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. In 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

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Keywords

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