SOMiMS - Topographic Mapping in the Model Space

Xinyue Chen; Yuan Shen; Eder Zavala; Krasimira Tsaneva-Atanasova; Thomas Upton; Georgina Russell; Peter Tino

doi:10.1007/978-3-030-91608-4_50

SOMiMS - Topographic Mapping in the Model Space

Xinyue Chen^*, Yuan Shen, Eder Zavala, Krasimira Tsaneva-Atanasova, Thomas Upton, Georgina Russell, Peter Tino

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

Abstract

Learning in the model space (LiMS) represents each observational unit (e.g. sparse and irregular time series) with a suitable model of it (point estimate), or a full posterior distribution over models. LiMS approaches take the mechanistic information of how the data is generated into account, thus enhancing the transparency and interpretability of the machine learning tools employed. In this paper we develop a novel topographic mapping in the model space and compare it with an extension of the Generative Topographic Mapping (GTM) to the model space. We demonstrate these two methods on a dataset of measurements taken on subjects in an adrenal steroid hormone deficiency study.

Original language	English
Title of host publication	Intelligent Data Engineering and Automated Learning - 22nd International Conference, IDEAL 2021, Proceedings
Editors	David Camacho, Peter Tino, Richard Allmendinger, Hujun Yin, Antonio J. Tallón-Ballesteros, Ke Tang, Sung-Bae Cho, Paulo Novais, Susana Nascimento
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	502-510
Number of pages	9
ISBN (Electronic)	9783030916084
ISBN (Print)	9783030916077
DOIs	https://doi.org/10.1007/978-3-030-91608-4_50
Publication status	Published - 23 Nov 2021
Event	22nd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2021 - Virtual, Online Duration: 25 Nov 2021 → 27 Nov 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13113 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	22nd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2021
City	Virtual, Online
Period	25/11/21 → 27/11/21

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Research Groups and Themes

Engineering Mathematics Research Group

Keywords

Irregular time series
LiMS
Sparse
Topographic mapping

Access to Document

10.1007/978-3-030-91608-4_50

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Persistent link

Cite this

Chen, X., Shen, Y., Zavala, E., Tsaneva-Atanasova, K., Upton, T., Russell, G., & Tino, P. (2021). SOMiMS - Topographic Mapping in the Model Space. In D. Camacho, P. Tino, R. Allmendinger, H. Yin, A. J. Tallón-Ballesteros, K. Tang, S.-B. Cho, P. Novais, & S. Nascimento (Eds.), Intelligent Data Engineering and Automated Learning - 22nd International Conference, IDEAL 2021, Proceedings (pp. 502-510). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13113 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-91608-4_50

Chen, Xinyue ; Shen, Yuan ; Zavala, Eder et al. / SOMiMS - Topographic Mapping in the Model Space. Intelligent Data Engineering and Automated Learning - 22nd International Conference, IDEAL 2021, Proceedings. editor / David Camacho ; Peter Tino ; Richard Allmendinger ; Hujun Yin ; Antonio J. Tallón-Ballesteros ; Ke Tang ; Sung-Bae Cho ; Paulo Novais ; Susana Nascimento. Springer Science and Business Media Deutschland GmbH, 2021. pp. 502-510 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Learning in the model space (LiMS) represents each observational unit (e.g. sparse and irregular time series) with a suitable model of it (point estimate), or a full posterior distribution over models. LiMS approaches take the mechanistic information of how the data is generated into account, thus enhancing the transparency and interpretability of the machine learning tools employed. In this paper we develop a novel topographic mapping in the model space and compare it with an extension of the Generative Topographic Mapping (GTM) to the model space. We demonstrate these two methods on a dataset of measurements taken on subjects in an adrenal steroid hormone deficiency study.",

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Chen, X, Shen, Y, Zavala, E, Tsaneva-Atanasova, K, Upton, T , Russell, G & Tino, P 2021, SOMiMS - Topographic Mapping in the Model Space. in D Camacho, P Tino, R Allmendinger, H Yin, AJ Tallón-Ballesteros, K Tang, S-B Cho, P Novais & S Nascimento (eds), Intelligent Data Engineering and Automated Learning - 22nd International Conference, IDEAL 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13113 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 502-510, 22nd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2021, Virtual, Online, 25/11/21. https://doi.org/10.1007/978-3-030-91608-4_50

SOMiMS - Topographic Mapping in the Model Space. / Chen, Xinyue; Shen, Yuan; Zavala, Eder et al.
Intelligent Data Engineering and Automated Learning - 22nd International Conference, IDEAL 2021, Proceedings. ed. / David Camacho; Peter Tino; Richard Allmendinger; Hujun Yin; Antonio J. Tallón-Ballesteros; Ke Tang; Sung-Bae Cho; Paulo Novais; Susana Nascimento. Springer Science and Business Media Deutschland GmbH, 2021. p. 502-510 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13113 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

TY - GEN

T1 - SOMiMS - Topographic Mapping in the Model Space

AU - Chen, Xinyue

AU - Shen, Yuan

AU - Zavala, Eder

AU - Tsaneva-Atanasova, Krasimira

AU - Upton, Thomas

AU - Russell, Georgina

AU - Tino, Peter

PY - 2021/11/23

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N2 - Learning in the model space (LiMS) represents each observational unit (e.g. sparse and irregular time series) with a suitable model of it (point estimate), or a full posterior distribution over models. LiMS approaches take the mechanistic information of how the data is generated into account, thus enhancing the transparency and interpretability of the machine learning tools employed. In this paper we develop a novel topographic mapping in the model space and compare it with an extension of the Generative Topographic Mapping (GTM) to the model space. We demonstrate these two methods on a dataset of measurements taken on subjects in an adrenal steroid hormone deficiency study.

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SN - 9783030916077

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Intelligent Data Engineering and Automated Learning - 22nd International Conference, IDEAL 2021, Proceedings

A2 - Camacho, David

A2 - Tino, Peter

A2 - Allmendinger, Richard

A2 - Yin, Hujun

A2 - Tallón-Ballesteros, Antonio J.

A2 - Tang, Ke

A2 - Cho, Sung-Bae

A2 - Novais, Paulo

A2 - Nascimento, Susana

PB - Springer Science and Business Media Deutschland GmbH

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ER -

Chen X, Shen Y, Zavala E, Tsaneva-Atanasova K, Upton T , Russell G et al. SOMiMS - Topographic Mapping in the Model Space. In Camacho D, Tino P, Allmendinger R, Yin H, Tallón-Ballesteros AJ, Tang K, Cho SB, Novais P, Nascimento S, editors, Intelligent Data Engineering and Automated Learning - 22nd International Conference, IDEAL 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 502-510. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-91608-4_50

SOMiMS - Topographic Mapping in the Model Space

Abstract

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Bibliographical note

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Keywords

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