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 |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9066973/ |
| _version_ | 1828112152734466048 |
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| author | Tiantian Xu Long Zhao |
| author_facet | Tiantian Xu Long Zhao |
| author_sort | Tiantian Xu |
| collection | DOAJ |
| description | 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 accompanying occurrences in real applications due to the high expense of manual annotation and label ambiguity. We call this “highly incomplete labels” problem. Such label incompleteness severely damages the inherent label structures and masks true label correlations. In this paper, we propose a novel structure-induced feature selection model to simultaneously identify the most discriminative features and recover the highly incomplete labels. To our best knowledge, it is the first attempt to explore the local density structure of data to capture the intricate feature-label dependency in the highly incomplete learning scenarios. Feature selection is guided by the label structure reconstruction, and highly incomplete labels are recovered via the structure transferred from feature space. In this elegant manner, the processes of selecting discriminative features and recovering incomplete labels are coupled in a unified optimization framework. Comprehensive experiments on public benchmark datasets demonstrate the superiority of the proposed approach. |
| first_indexed | 2024-04-11T11:46:06Z |
| format | Article |
| id | doaj.art-f74870eff9414586a3df0c8d7e2ae9fe |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T11:46:06Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f74870eff9414586a3df0c8d7e2ae9fe2022-12-22T04:25:37ZengIEEEIEEE Access2169-35362020-01-018712197123010.1109/ACCESS.2020.29879229066973A Structure-Induced Framework for Multi-Label Feature Selection With Highly Incomplete LabelsTiantian Xu0https://orcid.org/0000-0002-7737-4858Long Zhao1https://orcid.org/0000-0002-7284-0776School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, ChinaSchool of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, ChinaFeature 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 accompanying occurrences in real applications due to the high expense of manual annotation and label ambiguity. We call this “highly incomplete labels” problem. Such label incompleteness severely damages the inherent label structures and masks true label correlations. In this paper, we propose a novel structure-induced feature selection model to simultaneously identify the most discriminative features and recover the highly incomplete labels. To our best knowledge, it is the first attempt to explore the local density structure of data to capture the intricate feature-label dependency in the highly incomplete learning scenarios. Feature selection is guided by the label structure reconstruction, and highly incomplete labels are recovered via the structure transferred from feature space. In this elegant manner, the processes of selecting discriminative features and recovering incomplete labels are coupled in a unified optimization framework. Comprehensive experiments on public benchmark datasets demonstrate the superiority of the proposed approach.https://ieeexplore.ieee.org/document/9066973/Feature selectionmulti-label learningweakly-supervised learninglabel correlation |
| spellingShingle | Tiantian Xu Long Zhao A Structure-Induced Framework for Multi-Label Feature Selection With Highly Incomplete Labels IEEE Access Feature selection multi-label learning weakly-supervised learning label correlation |
| title | A Structure-Induced Framework for Multi-Label Feature Selection With Highly Incomplete Labels |
| title_full | A Structure-Induced Framework for Multi-Label Feature Selection With Highly Incomplete Labels |
| title_fullStr | A Structure-Induced Framework for Multi-Label Feature Selection With Highly Incomplete Labels |
| title_full_unstemmed | A Structure-Induced Framework for Multi-Label Feature Selection With Highly Incomplete Labels |
| title_short | A Structure-Induced Framework for Multi-Label Feature Selection With Highly Incomplete Labels |
| title_sort | structure induced framework for multi label feature selection with highly incomplete labels |
| topic | Feature selection multi-label learning weakly-supervised learning label correlation |
| url | https://ieeexplore.ieee.org/document/9066973/ |
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