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dc.contributor.authorHao, Yuxing
dc.contributor.authorLi, Huanjie
dc.contributor.authorHu, Guoqiang
dc.contributor.authorZhao, Wei
dc.contributor.authorCong, Fengyu
dc.date.accessioned2024-10-25T07:19:19Z
dc.date.available2024-10-25T07:19:19Z
dc.date.issued2024
dc.identifier.citationHao, Y., Li, H., Hu, G., Zhao, W., & Cong, F. (2024). Correcting variance and polarity indeterminacies of extracted components by canonical polyadic decomposition. <i> Brain-Apparatus Communication</i>, <i>3</i>(1), Article 2403477. <a href="https://doi.org/10.1080/27706710.2024.2403477" target="_blank">https://doi.org/10.1080/27706710.2024.2403477</a>
dc.identifier.otherCONVID_243225341
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/97691
dc.description.abstractBackground Back-projection has been used to correct the variance and polarity indeterminacies for the independent component analysis. The variance and polarity of the components are essential features of neuroscience studies. Objective This work extends the back-projection theory to canonical polyadic decomposition (CPD) for high-order tensors, aiming to correct the variance and polarity indeterminacies of the components extracted by CPD. Methods The tensor is reshaped into a matrix and decomposed using a suitable blind source separation algorithm. Subsequently, the coefficients are projected using back-projection theory, and other factor matrices are computed through a series of singular value decompositions of the back-projection matrix. Results By applying this method, the energy and polarity of each component are determined, effectively correcting the variance and polarity indeterminacies in CPD. The proposed method was validated using simulated tensor data and resting-state fMRI data. Conclusion Our proposed back-projection method for high-order tensors effectively corrects variance and polarity indeterminacies in CPD, offering a precise solution for calculating the energy and polarity required to extract meaningful features from neuroimaging data.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherTaylor & Francis
dc.relation.ispartofseriesBrain-Apparatus Communication
dc.rightsCC BY-NC 4.0
dc.subject.otherback-projection
dc.subject.otherblind source separation
dc.subject.othercanonical polyadic decomposition
dc.subject.othertensor
dc.titleCorrecting variance and polarity indeterminacies of extracted components by canonical polyadic decomposition
dc.typeresearch article
dc.identifier.urnURN:NBN:fi:jyu-202410256547
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.relation.issn2770-6710
dc.relation.numberinseries1
dc.relation.volume3
dc.type.versionpublishedVersion
dc.rights.copyright© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
dc.rights.accesslevelopenAccessfi
dc.type.publicationarticle
dc.subject.ysokomponentit
dc.subject.ysomatriisit
dc.subject.ysoaivotutkimus
dc.subject.ysoriippumattomien komponenttien analyysi
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p954
jyx.subject.urihttp://www.yso.fi/onto/yso/p18099
jyx.subject.urihttp://www.yso.fi/onto/yso/p23705
jyx.subject.urihttp://www.yso.fi/onto/yso/p38529
dc.rights.urlhttps://creativecommons.org/licenses/by-nc/4.0/
dc.relation.doi10.1080/27706710.2024.2403477
jyx.fundinginformationThis work was supported by the National Natural Science Foundation of China [grant numbers 91748105 & 81471742], the Science and Technology Planning Project of Liaoning Provincial [grant numbers 2022JH2/10700002 and 2021JH1/10400049], and the scholarship from China Scholarship Council (No. 202306060039).
dc.type.okmA1


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