Correcting variance and polarity indeterminacies of extracted components by canonical polyadic decomposition
Background 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 polyadi...
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| Format: | Article |
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Taylor & Francis Group
2024-12-01
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| Series: | Brain-Apparatus Communication |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/27706710.2024.2403477 |
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| author | Yuxing Hao Huanjie Li Guoqiang Hu Wei Zhao Fengyu Cong |
| author_facet | Yuxing Hao Huanjie Li Guoqiang Hu Wei Zhao Fengyu Cong |
| author_sort | Yuxing Hao |
| collection | DOAJ |
| description | Background 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. |
| format | Article |
| id | doaj-art-6d38a49d898844bcbe60867d8ffbc26c |
| institution | OA Journals |
| issn | 2770-6710 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Brain-Apparatus Communication |
| spelling | doaj-art-6d38a49d898844bcbe60867d8ffbc26c2025-08-20T01:57:54ZengTaylor & Francis GroupBrain-Apparatus Communication2770-67102024-12-013110.1080/27706710.2024.2403477Correcting variance and polarity indeterminacies of extracted components by canonical polyadic decompositionYuxing Hao0Huanjie Li1Guoqiang Hu2Wei Zhao3Fengyu Cong4School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, ChinaSchool of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, ChinaCollege of Artificial Intelligence, Dalian Maritime University, Dalian, ChinaSchool of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, ChinaSchool of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, Dalian, ChinaBackground 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.https://www.tandfonline.com/doi/10.1080/27706710.2024.2403477Back-projectionblind source separationcanonical polyadic decompositiontensor |
| spellingShingle | Yuxing Hao Huanjie Li Guoqiang Hu Wei Zhao Fengyu Cong Correcting variance and polarity indeterminacies of extracted components by canonical polyadic decomposition Brain-Apparatus Communication Back-projection blind source separation canonical polyadic decomposition tensor |
| title | Correcting variance and polarity indeterminacies of extracted components by canonical polyadic decomposition |
| title_full | Correcting variance and polarity indeterminacies of extracted components by canonical polyadic decomposition |
| title_fullStr | Correcting variance and polarity indeterminacies of extracted components by canonical polyadic decomposition |
| title_full_unstemmed | Correcting variance and polarity indeterminacies of extracted components by canonical polyadic decomposition |
| title_short | Correcting variance and polarity indeterminacies of extracted components by canonical polyadic decomposition |
| title_sort | correcting variance and polarity indeterminacies of extracted components by canonical polyadic decomposition |
| topic | Back-projection blind source separation canonical polyadic decomposition tensor |
| url | https://www.tandfonline.com/doi/10.1080/27706710.2024.2403477 |
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