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Change-Point Detection for Multi-Way Tensor-Based Frameworks
Graph-based change-point detection methods are often applied due to their advantages for using high-dimensional data. Most applications focus on extracting effective information of objects while ignoring their main features. However, in some applications, one may be interested in detecting objects w...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10137363/ https://www.ncbi.nlm.nih.gov/pubmed/37190340 http://dx.doi.org/10.3390/e25040552 |
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author | Qin, Shanshan Zhou, Ge Wu, Yuehua |
author_facet | Qin, Shanshan Zhou, Ge Wu, Yuehua |
author_sort | Qin, Shanshan |
collection | PubMed |
description | Graph-based change-point detection methods are often applied due to their advantages for using high-dimensional data. Most applications focus on extracting effective information of objects while ignoring their main features. However, in some applications, one may be interested in detecting objects with different features, such as color. Therefore, we propose a general graph-based change-point detection method under the multi-way tensor framework, aimed at detecting objects with different features that change in the distribution of one or more slices. Furthermore, considering that recorded tensor sequences may be vulnerable to natural disturbances, such as lighting in images or videos, we propose an improved method incorporating histogram equalization techniques to improve detection efficiency. Finally, through simulations and real data analysis, we show that the proposed methods achieve higher efficiency in detecting change-points. |
format | Online Article Text |
id | pubmed-10137363 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-101373632023-04-28 Change-Point Detection for Multi-Way Tensor-Based Frameworks Qin, Shanshan Zhou, Ge Wu, Yuehua Entropy (Basel) Article Graph-based change-point detection methods are often applied due to their advantages for using high-dimensional data. Most applications focus on extracting effective information of objects while ignoring their main features. However, in some applications, one may be interested in detecting objects with different features, such as color. Therefore, we propose a general graph-based change-point detection method under the multi-way tensor framework, aimed at detecting objects with different features that change in the distribution of one or more slices. Furthermore, considering that recorded tensor sequences may be vulnerable to natural disturbances, such as lighting in images or videos, we propose an improved method incorporating histogram equalization techniques to improve detection efficiency. Finally, through simulations and real data analysis, we show that the proposed methods achieve higher efficiency in detecting change-points. MDPI 2023-03-23 /pmc/articles/PMC10137363/ /pubmed/37190340 http://dx.doi.org/10.3390/e25040552 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Qin, Shanshan Zhou, Ge Wu, Yuehua Change-Point Detection for Multi-Way Tensor-Based Frameworks |
title | Change-Point Detection for Multi-Way Tensor-Based Frameworks |
title_full | Change-Point Detection for Multi-Way Tensor-Based Frameworks |
title_fullStr | Change-Point Detection for Multi-Way Tensor-Based Frameworks |
title_full_unstemmed | Change-Point Detection for Multi-Way Tensor-Based Frameworks |
title_short | Change-Point Detection for Multi-Way Tensor-Based Frameworks |
title_sort | change-point detection for multi-way tensor-based frameworks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10137363/ https://www.ncbi.nlm.nih.gov/pubmed/37190340 http://dx.doi.org/10.3390/e25040552 |
work_keys_str_mv | AT qinshanshan changepointdetectionformultiwaytensorbasedframeworks AT zhouge changepointdetectionformultiwaytensorbasedframeworks AT wuyuehua changepointdetectionformultiwaytensorbasedframeworks |