Finding the Most Interpretable MDS Rotation for Sparse Linear Models based on External Features

Adrien Bibal; Rebecca Marion; Benoît Frénay

Finding the Most Interpretable MDS Rotation for Sparse Linear Models based on External Features

Adrien Bibal, Rebecca Marion, Benoît Frénay

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

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Abstract

One approach to interpreting multidimensional scaling (MDS)
embeddings is to estimate a linear relationship between the MDS dimen-
sions and a set of external features. However, because MDS only preserves
distances between instances, the MDS embedding is invariant to rotation.
As a result, the weights characterizing this linear relationship are arbitrary
and difficult to interpret. This paper proposes a procedure for selecting
the most pertinent rotation for interpreting a 2D MDS embedding.

Original language	English
Title of host publication	26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Subtitle of host publication	ESANN 2018 : Bruges, Belgium, April 25, 26, 27, 2018
Editors	Michel Verleysen
Place of Publication	Louvain-la-Neuve
Publisher	CIACO
Pages	537-542
ISBN (Electronic)	9782875870476
Publication status	Published - 1 Jan 2018
Event	26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018) - Bruges, Bruges, Belgium Duration: 25 Apr 2018 → 27 Apr 2018

Conference

Conference	26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018)
Country/Territory	Belgium
City	Bruges
Period	25/04/18 → 27/04/18

Keywords

Machine learning
Interpretability
Dimensionality reduction
Multidimensional scaling
Multi-view
Sparsity
Lasso regularization

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2018-Bibal_Marion-Finding the Most Interpretable MDS Rotation for Sparse Linear Models based on External FeaturesAccepted author manuscript, 329 KB

Cite this

Bibal, Adrien ; Marion, Rebecca ; Frénay, Benoît. / Finding the Most Interpretable MDS Rotation for Sparse Linear Models based on External Features. 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning : ESANN 2018 : Bruges, Belgium, April 25, 26, 27, 2018. editor / Michel Verleysen. Louvain-la-Neuve : CIACO , 2018. pp. 537-542

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title = "Finding the Most Interpretable MDS Rotation for Sparse Linear Models based on External Features",

abstract = "One approach to interpreting multidimensional scaling (MDS)embeddings is to estimate a linear relationship between the MDS dimen-sions and a set of external features. However, because MDS only preservesdistances between instances, the MDS embedding is invariant to rotation.As a result, the weights characterizing this linear relationship are arbitraryand difficult to interpret. This paper proposes a procedure for selectingthe most pertinent rotation for interpreting a 2D MDS embedding.",

keywords = "Machine learning, Interpretability, Dimensionality reduction, Multidimensional scaling, Multi-view, Sparsity, Lasso regularization",

author = "Adrien Bibal and Rebecca Marion and Beno{\^i}t Fr{\'e}nay",

note = "Funding Information: The authors want to thank Nathan Nguyen from the Universite catholique de Louvain for having pointed to the need for this kind of procedure in psychology. The second author gratefully acknowledges financial support from the IAP Research Network P7/06 of the Belgian State (Belgian Science Policy), the Fonds special de recherche (Federation Wallonie-Bruxelles) and the Belgian Fund for Scientific Research (F.R.S.-FNRS, FRIA grant). Funding Information: The authors want to thank Nathan Nguyen from the Universit{\'e} catholique de Louvain for having pointed to the need for this kind of procedure in psychology. The second author gratefully acknowledges financial support from the IAP Research Network P7/06 of the Belgian State (Belgian Science Policy), the Fonds sp{\'e}cial de recherche (F{\'e}d{\'e}ration Wallonie-Bruxelles) and the Belgian Fund for Scientific Research (F.R.S.-FNRS, FRIA grant). Publisher Copyright: {\textcopyright} ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.; 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018) ; Conference date: 25-04-2018 Through 27-04-2018",

year = "2018",

month = jan,

day = "1",

language = "English",

pages = "537--542",

editor = "Michel Verleysen",

booktitle = "26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning",

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Bibal, A, Marion, R & Frénay, B 2018, Finding the Most Interpretable MDS Rotation for Sparse Linear Models based on External Features. in M Verleysen (ed.), 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning : ESANN 2018 : Bruges, Belgium, April 25, 26, 27, 2018. CIACO , Louvain-la-Neuve, pp. 537-542, 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018), Bruges, Belgium, 25/04/18.

Finding the Most Interpretable MDS Rotation for Sparse Linear Models based on External Features. / Bibal, Adrien; Marion, Rebecca; Frénay, Benoît.

26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning : ESANN 2018 : Bruges, Belgium, April 25, 26, 27, 2018. ed. / Michel Verleysen. Louvain-la-Neuve : CIACO , 2018. p. 537-542.

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

TY - GEN

T1 - Finding the Most Interpretable MDS Rotation for Sparse Linear Models based on External Features

AU - Bibal, Adrien

AU - Marion, Rebecca

AU - Frénay, Benoît

N1 - Funding Information: The authors want to thank Nathan Nguyen from the Universite catholique de Louvain for having pointed to the need for this kind of procedure in psychology. The second author gratefully acknowledges financial support from the IAP Research Network P7/06 of the Belgian State (Belgian Science Policy), the Fonds special de recherche (Federation Wallonie-Bruxelles) and the Belgian Fund for Scientific Research (F.R.S.-FNRS, FRIA grant). Funding Information: The authors want to thank Nathan Nguyen from the Université catholique de Louvain for having pointed to the need for this kind of procedure in psychology. The second author gratefully acknowledges financial support from the IAP Research Network P7/06 of the Belgian State (Belgian Science Policy), the Fonds spécial de recherche (Fédération Wallonie-Bruxelles) and the Belgian Fund for Scientific Research (F.R.S.-FNRS, FRIA grant). Publisher Copyright: © ESANN 2018 - Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.

PY - 2018/1/1

Y1 - 2018/1/1

N2 - One approach to interpreting multidimensional scaling (MDS)embeddings is to estimate a linear relationship between the MDS dimen-sions and a set of external features. However, because MDS only preservesdistances between instances, the MDS embedding is invariant to rotation.As a result, the weights characterizing this linear relationship are arbitraryand difficult to interpret. This paper proposes a procedure for selectingthe most pertinent rotation for interpreting a 2D MDS embedding.

AB - One approach to interpreting multidimensional scaling (MDS)embeddings is to estimate a linear relationship between the MDS dimen-sions and a set of external features. However, because MDS only preservesdistances between instances, the MDS embedding is invariant to rotation.As a result, the weights characterizing this linear relationship are arbitraryand difficult to interpret. This paper proposes a procedure for selectingthe most pertinent rotation for interpreting a 2D MDS embedding.

KW - Machine learning

KW - Interpretability

KW - Dimensionality reduction

KW - Multidimensional scaling

KW - Multi-view

KW - Sparsity

KW - Lasso regularization

UR - http://www.scopus.com/inward/record.url?scp=85061840537&partnerID=8YFLogxK

M3 - Conference contribution

SP - 537

EP - 542

BT - 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

A2 - Verleysen, Michel

PB - CIACO

CY - Louvain-la-Neuve

T2 - 26th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2018)

Y2 - 25 April 2018 through 27 April 2018

ER -

Finding the Most Interpretable MDS Rotation for Sparse Linear Models based on External Features

Abstract

Conference

Keywords

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

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Finding the Most Interpretable MDS Rotation for Sparse Linear Models based on External Features

Abstract

Conference

Keywords

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Student theses

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

Cite this