Latent space unsupervised semantic segmentation

The development of compact and energy-efficient wearable sensors has led to an increase in the availability of biosignals. To effectively and efficiently analyze continuously recorded and multidimensional time series at scale, the ability to perform meaningful unsupervised data segmentation is an au...

Descripción completa

Detalles Bibliográficos
Autores principales: Strommen, Knut J., Tørresen, Jim, Côté-Allard, Ulysse
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10166858/
https://www.ncbi.nlm.nih.gov/pubmed/37179829
http://dx.doi.org/10.3389/fphys.2023.1151312
_version_ 1785038533440831488
author Strommen, Knut J.
Tørresen, Jim
Côté-Allard, Ulysse
author_facet Strommen, Knut J.
Tørresen, Jim
Côté-Allard, Ulysse
author_sort Strommen, Knut J.
collection PubMed
description The development of compact and energy-efficient wearable sensors has led to an increase in the availability of biosignals. To effectively and efficiently analyze continuously recorded and multidimensional time series at scale, the ability to perform meaningful unsupervised data segmentation is an auspicious target. A common way to achieve this is to identify change-points within the time series as the segmentation basis. However, traditional change-point detection algorithms often come with drawbacks, limiting their real-world applicability. Notably, they generally rely on the complete time series to be available and thus cannot be used for real-time applications. Another common limitation is that they poorly (or cannot) handle the segmentation of multidimensional time series. Consequently, the main contribution of this work is to propose a novel unsupervised segmentation algorithm for multidimensional time series named Latent Space Unsupervised Semantic Segmentation (LS-USS), which was designed to easily work with both online and batch data. Latent Space Unsupervised Semantic Segmentation addresses the challenge of multivariate change-point detection by utilizing an autoencoder to learn a 1-dimensional latent space on which change-point detection is then performed. To address the challenge of real-time time series segmentation, this work introduces the Local Threshold Extraction Algorithm (LTEA) and a “batch collapse” algorithm. The “batch collapse” algorithm enables Latent Space Unsupervised Semantic Segmentation to process streaming data by dividing it into manageable batches, while Local Threshold Extraction Algorithm is employed to detect change-points in the time series whenever the computed metric by Latent Space Unsupervised Semantic Segmentation exceeds a predefined threshold. By using these algorithms in combination, our approach is able to accurately segment time series data in real-time, making it well-suited for applications where timely detection of changes is critical. When evaluating Latent Space Unsupervised Semantic Segmentation on a variety of real-world datasets the Latent Space Unsupervised Semantic Segmentation systematically achieves equal or better performance than other state-of-the-art change-point detection algorithms it is compared to in both offline and real-time settings.
format Online
Article
Text
id pubmed-10166858
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-101668582023-05-10 Latent space unsupervised semantic segmentation Strommen, Knut J. Tørresen, Jim Côté-Allard, Ulysse Front Physiol Physiology The development of compact and energy-efficient wearable sensors has led to an increase in the availability of biosignals. To effectively and efficiently analyze continuously recorded and multidimensional time series at scale, the ability to perform meaningful unsupervised data segmentation is an auspicious target. A common way to achieve this is to identify change-points within the time series as the segmentation basis. However, traditional change-point detection algorithms often come with drawbacks, limiting their real-world applicability. Notably, they generally rely on the complete time series to be available and thus cannot be used for real-time applications. Another common limitation is that they poorly (or cannot) handle the segmentation of multidimensional time series. Consequently, the main contribution of this work is to propose a novel unsupervised segmentation algorithm for multidimensional time series named Latent Space Unsupervised Semantic Segmentation (LS-USS), which was designed to easily work with both online and batch data. Latent Space Unsupervised Semantic Segmentation addresses the challenge of multivariate change-point detection by utilizing an autoencoder to learn a 1-dimensional latent space on which change-point detection is then performed. To address the challenge of real-time time series segmentation, this work introduces the Local Threshold Extraction Algorithm (LTEA) and a “batch collapse” algorithm. The “batch collapse” algorithm enables Latent Space Unsupervised Semantic Segmentation to process streaming data by dividing it into manageable batches, while Local Threshold Extraction Algorithm is employed to detect change-points in the time series whenever the computed metric by Latent Space Unsupervised Semantic Segmentation exceeds a predefined threshold. By using these algorithms in combination, our approach is able to accurately segment time series data in real-time, making it well-suited for applications where timely detection of changes is critical. When evaluating Latent Space Unsupervised Semantic Segmentation on a variety of real-world datasets the Latent Space Unsupervised Semantic Segmentation systematically achieves equal or better performance than other state-of-the-art change-point detection algorithms it is compared to in both offline and real-time settings. Frontiers Media S.A. 2023-04-25 /pmc/articles/PMC10166858/ /pubmed/37179829 http://dx.doi.org/10.3389/fphys.2023.1151312 Text en Copyright © 2023 Strommen, Tørresen and Côté-Allard. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Physiology
Strommen, Knut J.
Tørresen, Jim
Côté-Allard, Ulysse
Latent space unsupervised semantic segmentation
title Latent space unsupervised semantic segmentation
title_full Latent space unsupervised semantic segmentation
title_fullStr Latent space unsupervised semantic segmentation
title_full_unstemmed Latent space unsupervised semantic segmentation
title_short Latent space unsupervised semantic segmentation
title_sort latent space unsupervised semantic segmentation
topic Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10166858/
https://www.ncbi.nlm.nih.gov/pubmed/37179829
http://dx.doi.org/10.3389/fphys.2023.1151312
work_keys_str_mv AT strommenknutj latentspaceunsupervisedsemanticsegmentation
AT tørresenjim latentspaceunsupervisedsemanticsegmentation
AT coteallardulysse latentspaceunsupervisedsemanticsegmentation