{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:44:19Z","timestamp":1765547059636,"version":"build-2065373602"},"reference-count":7,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,3,23]],"date-time":"2023-03-23T00:00:00Z","timestamp":1679529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Tianyuan Fund for Mathematics, National Natural Science Foundation of China","award":["12226333","12201454","RGPIN-2017-05720"],"award-info":[{"award-number":["12226333","12201454","RGPIN-2017-05720"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12226333","12201454","RGPIN-2017-05720"],"award-info":[{"award-number":["12226333","12201454","RGPIN-2017-05720"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science and Engineering Research Council of Canada","award":["12226333","12201454","RGPIN-2017-05720"],"award-info":[{"award-number":["12226333","12201454","RGPIN-2017-05720"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/e25040552","type":"journal-article","created":{"date-parts":[[2023,3,24]],"date-time":"2023-03-24T03:16:46Z","timestamp":1679627806000},"page":"552","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Change-Point Detection for Multi-Way Tensor-Based Frameworks"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1325-1936","authenticated-orcid":false,"given":"Shanshan","family":"Qin","sequence":"first","affiliation":[{"name":"School of Statistics, Tianjin University of Finance and Economics, Tianjin 300222, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0353-870X","authenticated-orcid":false,"given":"Ge","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Statistics, Tianjin University of Finance and Economics, Tianjin 300222, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8858-1289","authenticated-orcid":false,"given":"Yuehua","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3873","DOI":"10.1073\/pnas.1702654114","article-title":"Consistent and powerful graph-based change-point test for high-dimensional data","volume":"114","author":"Shi","year":"2017","journal-title":"Proc. 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