SOMiMS - topographic mapping in the model space

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning – IDEAL 2021
Subtitle of host publication22nd International Conference, IDEAL 2021 Manchester, UK, November 25–27, 2021 Proceedings
EditorsHujun Yin, David Camacho, Peter Tino, Richard Allmendinger, Antonio J. Tallón-Ballesteros, Ke Tang, Sung-Bae Cho, Paulo Novais, Susana Nascimento
PublisherSpringer Nature
Pages502-510
Number of pages9
ISBN (Electronic)9783030916084
ISBN (Print)9783030916077
DOIs
Publication statusAccepted/In press - 6 Sep 2021
EventThe 22nd International Conference on Intelligent Data Engineering and Automated Learning (IDEAL) - Manchester, United Kingdom
Duration: 25 Nov 202127 Nov 2021
Conference number: 22nd

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume13113
ISSN (Print)0302-9743

Conference

ConferenceThe 22nd International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)
Abbreviated titleIDEAL 2021
Country/TerritoryUnited Kingdom
CityManchester
Period25/11/2127/11/21

Keywords

  • LiMS
  • Topographic mapping
  • Sparse
  • Irregular time series

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