Structured insight: an innovative disambiguation paradigm for semi-supervised partial label learning
Abstract Semi-Supervised Partial Label Learning (SPLL) aims to learn from both partial label data where each instance is associated with a candidate label set and unlabeled data. Most SPLL methods work by generating pseudo-candidate labels for unsupervised data. Since the pseudo candidate labels are...
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| Format: | Article |
| Language: | English |
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Springer
2025-07-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
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| Online Access: | https://doi.org/10.1007/s44443-025-00119-x |
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| author | Xin Niu Jing Chai Musaed Alhussein Khursheed Aurangzeb |
| author_facet | Xin Niu Jing Chai Musaed Alhussein Khursheed Aurangzeb |
| author_sort | Xin Niu |
| collection | DOAJ |
| description | Abstract Semi-Supervised Partial Label Learning (SPLL) aims to learn from both partial label data where each instance is associated with a candidate label set and unlabeled data. Most SPLL methods work by generating pseudo-candidate labels for unsupervised data. Since the pseudo candidate labels are assigned manually to unlabeled data, sometimes they are not as accurate as the candidate labels of partial label data. However, many SPLL methods adopted unique disambiguation strategy to tackle both partial label data and unlabeled data, which often fail to account for the intrinsic structural differences between partial label data and unlabeled data, resulting in suboptimal disambiguation and degraded performance. To tackle this problem, we propose a Structured Insight-driven Disambiguation Paradigm (SIDP). SIDP disambiguates partial label data via feature-label consistency, whereas employs label expansion to generate pseudo candidate labels for unlabeled data, followed by a refinement through class perception. At last, SIDP generates the final confidence matrix by integrating the above processes via label propagation. Extensive experiments on real-world datasets demonstrate that SIDP significantly outperforms several state-of-the-art competing methods in terms of classification accuracy. |
| format | Article |
| id | doaj-art-37fae4ca12804d2b87af4cf0e37ee436 |
| institution | Kabale University |
| issn | 1319-1578 2213-1248 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj-art-37fae4ca12804d2b87af4cf0e37ee4362025-08-20T03:43:34ZengSpringerJournal of King Saud University: Computer and Information Sciences1319-15782213-12482025-07-0137612010.1007/s44443-025-00119-xStructured insight: an innovative disambiguation paradigm for semi-supervised partial label learningXin Niu0Jing Chai1Musaed Alhussein2Khursheed Aurangzeb3School of Information Science and Engineering, Yunnan UniversitySchool of Information Science and Engineering, Yunnan UniversityDepartment of Computer Engineering, College of Computer and Information SciencesDepartment of Computer Engineering, College of Computer and Information SciencesAbstract Semi-Supervised Partial Label Learning (SPLL) aims to learn from both partial label data where each instance is associated with a candidate label set and unlabeled data. Most SPLL methods work by generating pseudo-candidate labels for unsupervised data. Since the pseudo candidate labels are assigned manually to unlabeled data, sometimes they are not as accurate as the candidate labels of partial label data. However, many SPLL methods adopted unique disambiguation strategy to tackle both partial label data and unlabeled data, which often fail to account for the intrinsic structural differences between partial label data and unlabeled data, resulting in suboptimal disambiguation and degraded performance. To tackle this problem, we propose a Structured Insight-driven Disambiguation Paradigm (SIDP). SIDP disambiguates partial label data via feature-label consistency, whereas employs label expansion to generate pseudo candidate labels for unlabeled data, followed by a refinement through class perception. At last, SIDP generates the final confidence matrix by integrating the above processes via label propagation. Extensive experiments on real-world datasets demonstrate that SIDP significantly outperforms several state-of-the-art competing methods in terms of classification accuracy.https://doi.org/10.1007/s44443-025-00119-xPartial label learningSemi-supervised learningPseudo candidate labelsConfidence matrix |
| spellingShingle | Xin Niu Jing Chai Musaed Alhussein Khursheed Aurangzeb Structured insight: an innovative disambiguation paradigm for semi-supervised partial label learning Journal of King Saud University: Computer and Information Sciences Partial label learning Semi-supervised learning Pseudo candidate labels Confidence matrix |
| title | Structured insight: an innovative disambiguation paradigm for semi-supervised partial label learning |
| title_full | Structured insight: an innovative disambiguation paradigm for semi-supervised partial label learning |
| title_fullStr | Structured insight: an innovative disambiguation paradigm for semi-supervised partial label learning |
| title_full_unstemmed | Structured insight: an innovative disambiguation paradigm for semi-supervised partial label learning |
| title_short | Structured insight: an innovative disambiguation paradigm for semi-supervised partial label learning |
| title_sort | structured insight an innovative disambiguation paradigm for semi supervised partial label learning |
| topic | Partial label learning Semi-supervised learning Pseudo candidate labels Confidence matrix |
| url | https://doi.org/10.1007/s44443-025-00119-x |
| work_keys_str_mv | AT xinniu structuredinsightaninnovativedisambiguationparadigmforsemisupervisedpartiallabellearning AT jingchai structuredinsightaninnovativedisambiguationparadigmforsemisupervisedpartiallabellearning AT musaedalhussein structuredinsightaninnovativedisambiguationparadigmforsemisupervisedpartiallabellearning AT khursheedaurangzeb structuredinsightaninnovativedisambiguationparadigmforsemisupervisedpartiallabellearning |