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

Division of Integrative Physiology

Yale School of Public Health
University of Birmingham

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

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 – IDEAL 2021
Subtitle of host publication	22nd International Conference, IDEAL 2021, Manchester, UK, November 25–27, 2021, Proceedings
Editors	Hujun Yin, David Camacho, Peter Tino, Richard Allmendinger, Antonio J. Tallón-Ballesteros, Ke Tang, Sung-Bae Cho, Paulo Novais, Susana Nascimento
Place of Publication	Cham
Publisher	Springer Cham
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 - 21 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
Publisher	Springer
Volume	13113
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

Keywords

LiMS
topographic mapping
sparse
irregular time series

Access to Document

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

Centre for Biological Timing
Lucas, R. (PI), Bechtold, D. (PI), Fustin, J.-M. (PI), Ashe, H. (PI), Brown, T. (PI), Blaikley, J. (PI), Brass, A. (PI), Chandola, T. (PI), Durrington, H. (PI), Else, K. (PI), Hepworth, M. (PI), Hunter, L. (PI), Kadler, K. (PI), Kitchen, G. (PI), Loudon, A. (PI), Macdonald, A. (PI), Mcbeth, J. (PI), Milosavljevic, N. (PI), Rattray, M. (PI), Rutter, M. (PI), Sharrocks, A. (PI), Spiller, D. (PI), Storchi, R. (PI), Belle, M. (PI), Meng, Q.-J. (PI), Allen, A. (PI), Dixon, W. (PI), Gibbs, J. (PI), Hazel, A. (PI), Papalopulu, N. (PI), Ray, D. (PI), White, M. (PI), Chang, J. (PI), Bano Otalora, B. (CoI), Zavala, E. (PI), Orlowska-Feuer, P. (PI) & Didikoglu, A. (PI)
Project: Research

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 H. Yin, D. Camacho, P. Tino, R. Allmendinger, A. J. Tallón-Ballesteros, K. Tang, S.-B. Cho, P. Novais, & S. Nascimento (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2021: 22nd International Conference, IDEAL 2021, Manchester, UK, November 25–27, 2021, Proceedings (pp. 502–510). (Lecture Notes in Computer Science ; Vol. 13113). Springer Cham. 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 – IDEAL 2021: 22nd International Conference, IDEAL 2021, Manchester, UK, November 25–27, 2021, Proceedings. editor / Hujun Yin ; David Camacho ; Peter Tino ; Richard Allmendinger ; Antonio J. Tallón-Ballesteros ; Ke Tang ; Sung-Bae Cho ; Paulo Novais ; Susana Nascimento. Cham : Springer Cham, 2021. pp. 502–510 (Lecture Notes in Computer Science ).

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title = "SOMiMS - Topographic Mapping in the Model Space",

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.",

keywords = "LiMS, topographic mapping, sparse, irregular time series",

author = "Xinyue Chen and Yuan Shen and Eder Zavala and Krasimira Tsaneva-Atanasova and Thomas Upton and Georgina Russell and Peter Tino",

year = "2021",

month = nov,

day = "21",

doi = "10.1007/978-3-030-91608-4\_50",

language = "English",

isbn = "9783030916077",

series = "Lecture Notes in Computer Science ",

publisher = "Springer Cham",

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editor = "Hujun Yin and David Camacho and Peter Tino and Allmendinger, \{Richard \} and Tall{\'o}n-Ballesteros, \{Antonio J.\} and Ke Tang and Sung-Bae Cho and Paulo Novais and Susana Nascimento",

booktitle = "Intelligent Data Engineering and Automated Learning {\textendash} IDEAL 2021",

address = "Switzerland",

note = "22nd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2021 ; Conference date: 25-11-2021 Through 27-11-2021",

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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 H Yin, D Camacho, P Tino, R Allmendinger, AJ Tallón-Ballesteros, K Tang, S-B Cho, P Novais & S Nascimento (eds), Intelligent Data Engineering and Automated Learning – IDEAL 2021: 22nd International Conference, IDEAL 2021, Manchester, UK, November 25–27, 2021, Proceedings. Lecture Notes in Computer Science , vol. 13113, Springer Cham, Cham, 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 – IDEAL 2021: 22nd International Conference, IDEAL 2021, Manchester, UK, November 25–27, 2021, Proceedings. ed. / Hujun Yin; David Camacho; Peter Tino; Richard Allmendinger; Antonio J. Tallón-Ballesteros; Ke Tang; Sung-Bae Cho; Paulo Novais; Susana Nascimento. Cham: Springer Cham, 2021. p. 502–510 (Lecture Notes in Computer Science ; Vol. 13113).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

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 - 2021/11/21

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AB - 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.

KW - LiMS

KW - topographic mapping

KW - sparse

KW - irregular time series

U2 - 10.1007/978-3-030-91608-4_50

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M3 - Conference contribution

SN - 9783030916077

T3 - Lecture Notes in Computer Science

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EP - 510

BT - Intelligent Data Engineering and Automated Learning – IDEAL 2021

A2 - Yin, Hujun

A2 - Camacho, David

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A2 - Allmendinger, Richard

A2 - Tallón-Ballesteros, Antonio J.

A2 - Tang, Ke

A2 - Cho, Sung-Bae

A2 - Novais, Paulo

A2 - Nascimento, Susana

PB - Springer Cham

CY - Cham

T2 - 22nd International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2021

Y2 - 25 November 2021 through 27 November 2021

ER -

Chen X, Shen Y, Zavala E, Tsaneva-Atanasova K, Upton T, Russell G et al. SOMiMS - Topographic Mapping in the Model Space. In Yin H, Camacho D, Tino P, Allmendinger R, Tallón-Ballesteros AJ, Tang K, Cho SB, Novais P, Nascimento S, editors, Intelligent Data Engineering and Automated Learning – IDEAL 2021: 22nd International Conference, IDEAL 2021, Manchester, UK, November 25–27, 2021, Proceedings. Cham: Springer Cham. 2021. p. 502–510. (Lecture Notes in Computer Science ). doi: 10.1007/978-3-030-91608-4_50

SOMiMS - Topographic Mapping in the Model Space

Abstract

Publication series

Conference

Keywords

Access to Document

Fingerprint

Projects

Centre for Biological Timing

Cite this