Multi-modal feature selection with anchor graph for Alzheimer's disease
In Alzheimer's disease, the researchers found that if the patients were treated at the early stage of the disease, it could effectively delay the development of the disease. At present, multi-modal feature selection is widely used in the early diagnosis of Alzheimer's disease. However, exi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-11-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2022.1036244/full |
| _version_ | 1798023216416948224 |
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| author | Jiaye Li Hang Xu Hao Yu Zhihao Jiang Lei Zhu |
| author_facet | Jiaye Li Hang Xu Hao Yu Zhihao Jiang Lei Zhu |
| author_sort | Jiaye Li |
| collection | DOAJ |
| description | In Alzheimer's disease, the researchers found that if the patients were treated at the early stage of the disease, it could effectively delay the development of the disease. At present, multi-modal feature selection is widely used in the early diagnosis of Alzheimer's disease. However, existing multi-modal feature selection algorithms focus on learning the internal information of multiple modalities. They ignore the relationship between modalities, the importance of each modality and the local structure in the multi-modal data. In this paper, we propose a multi-modal feature selection algorithm with anchor graph for Alzheimer's disease. Specifically, we first use the least square loss and l2,1−norm to obtain the weight of the feature under each modality. Then we embed a modal weight factor into the objective function to obtain the importance of each modality. Finally, we use anchor graph to quickly learn the local structure information in multi-modal data. In addition, we also verify the validity of the proposed algorithm on the published ADNI dataset. |
| first_indexed | 2024-04-11T17:42:48Z |
| format | Article |
| id | doaj.art-db6c388b5f2d4a4f82336b2147e39466 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-04-11T17:42:48Z |
| publishDate | 2022-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-db6c388b5f2d4a4f82336b2147e394662022-12-22T04:11:27ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2022-11-011610.3389/fnins.2022.10362441036244Multi-modal feature selection with anchor graph for Alzheimer's diseaseJiaye Li0Hang Xu1Hao Yu2Zhihao Jiang3Lei Zhu4School of Computer Science and Engineering, Central South University, Changsha, ChinaSchool of Computer Science and Engineering, Central South University, Changsha, ChinaSchool of Computer Science and Engineering, Central South University, Changsha, ChinaCollege of Computer Science and Electronic Engineering, Hunan University, Changsha, ChinaCollege of Information and Intelligence, Hunan Agricultural University, Changsha, ChinaIn Alzheimer's disease, the researchers found that if the patients were treated at the early stage of the disease, it could effectively delay the development of the disease. At present, multi-modal feature selection is widely used in the early diagnosis of Alzheimer's disease. However, existing multi-modal feature selection algorithms focus on learning the internal information of multiple modalities. They ignore the relationship between modalities, the importance of each modality and the local structure in the multi-modal data. In this paper, we propose a multi-modal feature selection algorithm with anchor graph for Alzheimer's disease. Specifically, we first use the least square loss and l2,1−norm to obtain the weight of the feature under each modality. Then we embed a modal weight factor into the objective function to obtain the importance of each modality. Finally, we use anchor graph to quickly learn the local structure information in multi-modal data. In addition, we also verify the validity of the proposed algorithm on the published ADNI dataset.https://www.frontiersin.org/articles/10.3389/fnins.2022.1036244/fullAlzheimer's diseaseanchor graphlocal structuremulti-modalfeature selection |
| spellingShingle | Jiaye Li Hang Xu Hao Yu Zhihao Jiang Lei Zhu Multi-modal feature selection with anchor graph for Alzheimer's disease Frontiers in Neuroscience Alzheimer's disease anchor graph local structure multi-modal feature selection |
| title | Multi-modal feature selection with anchor graph for Alzheimer's disease |
| title_full | Multi-modal feature selection with anchor graph for Alzheimer's disease |
| title_fullStr | Multi-modal feature selection with anchor graph for Alzheimer's disease |
| title_full_unstemmed | Multi-modal feature selection with anchor graph for Alzheimer's disease |
| title_short | Multi-modal feature selection with anchor graph for Alzheimer's disease |
| title_sort | multi modal feature selection with anchor graph for alzheimer s disease |
| topic | Alzheimer's disease anchor graph local structure multi-modal feature selection |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2022.1036244/full |
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