A Structure-Induced Framework for Multi-Label Feature Selection With Highly Incomplete Labels
Feature selection has shown significant promise in improving the effectiveness of multi- label learning by constructing a reduced feature space. Previous studies typically assume that label assignment is complete or partially complete; however, missing-label and unlabeled data are commonplace and ac...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9066973/ |