Multi-View Multi-Label Learning With View-Label-Specific Features
In multi-view multi-label learning, each object is represented by multiple data views, and belongs to multiple class labels simultaneously. Generally, all the data views have a contribution to the multi-label learning task, but their contributions are different. Besides, for each data view, each cla...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8769836/ |
| _version_ | 1818668279701438464 |
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| author | Jun Huang Xiwen Qu Guorong Li Feng Qin Xiao Zheng Qingming Huang |
| author_facet | Jun Huang Xiwen Qu Guorong Li Feng Qin Xiao Zheng Qingming Huang |
| author_sort | Jun Huang |
| collection | DOAJ |
| description | In multi-view multi-label learning, each object is represented by multiple data views, and belongs to multiple class labels simultaneously. Generally, all the data views have a contribution to the multi-label learning task, but their contributions are different. Besides, for each data view, each class label is only associated with a subset data features, and different features have different contributions to each class label. In this paper, we propose a novel framework VLSF for multi-view multi-label learning, i.e., multi-view multi-label learning with View-Label-Specific Features. Specifically, we first learn a low dimensional label-specific data representation for each data view and construct a multi-label classification model based on it by exploiting label correlations and view consensus, and learn the contribution weight of each data view to multi-label learning task for all the class labels jointly. Then, the final prediction can be made by combing the prediction results of all the classifiers and the learned contribution weights. The extensive comparison experiments with the state-of-the-art approaches manifest the effectiveness of the proposed method VLSF. |
| first_indexed | 2024-12-17T06:33:48Z |
| format | Article |
| id | doaj.art-7b69ca2628f54676b673873cec2e339d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-17T06:33:48Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7b69ca2628f54676b673873cec2e339d2022-12-21T22:00:04ZengIEEEIEEE Access2169-35362019-01-01710097910099210.1109/ACCESS.2019.29304688769836Multi-View Multi-Label Learning With View-Label-Specific FeaturesJun Huang0https://orcid.org/0000-0002-2022-5747Xiwen Qu1Guorong Li2Feng Qin3Xiao Zheng4Qingming Huang5School of Computer Science and Technology, Anhui University of Technology, Maanshan, ChinaSchool of Computer Science and Technology, Anhui University of Technology, Maanshan, ChinaSchool of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, ChinaSchool of Computer Science and Technology, Anhui University of Technology, Maanshan, ChinaSchool of Computer Science and Technology, Anhui University of Technology, Maanshan, ChinaSchool of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, ChinaIn multi-view multi-label learning, each object is represented by multiple data views, and belongs to multiple class labels simultaneously. Generally, all the data views have a contribution to the multi-label learning task, but their contributions are different. Besides, for each data view, each class label is only associated with a subset data features, and different features have different contributions to each class label. In this paper, we propose a novel framework VLSF for multi-view multi-label learning, i.e., multi-view multi-label learning with View-Label-Specific Features. Specifically, we first learn a low dimensional label-specific data representation for each data view and construct a multi-label classification model based on it by exploiting label correlations and view consensus, and learn the contribution weight of each data view to multi-label learning task for all the class labels jointly. Then, the final prediction can be made by combing the prediction results of all the classifiers and the learned contribution weights. The extensive comparison experiments with the state-of-the-art approaches manifest the effectiveness of the proposed method VLSF.https://ieeexplore.ieee.org/document/8769836/Multi-label learningmulti-view learningview-label-specific features |
| spellingShingle | Jun Huang Xiwen Qu Guorong Li Feng Qin Xiao Zheng Qingming Huang Multi-View Multi-Label Learning With View-Label-Specific Features IEEE Access Multi-label learning multi-view learning view-label-specific features |
| title | Multi-View Multi-Label Learning With View-Label-Specific Features |
| title_full | Multi-View Multi-Label Learning With View-Label-Specific Features |
| title_fullStr | Multi-View Multi-Label Learning With View-Label-Specific Features |
| title_full_unstemmed | Multi-View Multi-Label Learning With View-Label-Specific Features |
| title_short | Multi-View Multi-Label Learning With View-Label-Specific Features |
| title_sort | multi view multi label learning with view label specific features |
| topic | Multi-label learning multi-view learning view-label-specific features |
| url | https://ieeexplore.ieee.org/document/8769836/ |
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