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 |
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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 |
Publisher | Springer Nature |
Pages | 502-510 |
Number of pages | 9 |
ISBN (Electronic) | 9783030916084 |
ISBN (Print) | 9783030916077 |
DOIs | |
Publication status | Published - 23 Nov 2022 |
Event | The 22nd International Conference on Intelligent Data Engineering and Automated Learning (IDEAL) - Manchester, United Kingdom Duration: 25 Nov 2021 → 27 Nov 2021 Conference number: 22nd |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer |
Volume | 13113 |
ISSN (Print) | 0302-9743 |
Conference
Conference | The 22nd International Conference on Intelligent Data Engineering and Automated Learning (IDEAL) |
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Abbreviated title | IDEAL 2021 |
Country/Territory | United Kingdom |
City | Manchester |
Period | 25/11/21 → 27/11/21 |
Keywords
- LiMS
- Topographic mapping
- Sparse
- Irregular time series