A Unified Framework for Graph-Based Multi-View Partial Multi-Label Learning
Multi-view partial multi-label learning (MVPML) is a fundenmental problem where each sample is linked to multiple kinds of features and candidate labels, including ground-truth and noise labels. The key problem of MVPML is how to manipulate the multiple features and recover the ground-truth labels f...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10113221/ |
| _version_ | 1827941145197412352 |
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| author | Jiazheng Yuan Wei Liu Zhibin Gu Songhe Feng |
| author_facet | Jiazheng Yuan Wei Liu Zhibin Gu Songhe Feng |
| author_sort | Jiazheng Yuan |
| collection | DOAJ |
| description | Multi-view partial multi-label learning (MVPML) is a fundenmental problem where each sample is linked to multiple kinds of features and candidate labels, including ground-truth and noise labels. The key problem of MVPML is how to manipulate the multiple features and recover the ground-truth labels from candidate label set. To this end, this study designs a novel Graph-based Multi-view Partial Multi-label model named as GMPM, which combines the multi-view information detection, valuable label selection and multi-label predictor model learning into a unified optimization model. To be specific, GMPM first exploits the consensus information across multiple views by learning the view-specific similarity graph and fuses multiple graphs into a target one. Then, we divide the observed label set into two parts: the ground-truth part and the noise part, where the latter is associated with a sparse constraint to make sure the former is clean. Furthermore, we embed the learned unified similarity graph into the process of label disambiguation to restore a more reliable ground-truth label matrix. Finally, the resulting multi-label predictive model is learned with the help of ground-truth label matrix. Extensive experiments on six common used datasets demonstrate that the proposed GMPM achieves comparable performance over the state-of-the-arts. |
| first_indexed | 2024-03-13T09:31:51Z |
| format | Article |
| id | doaj.art-e1b06b1d73cf44988c5e720c292880c0 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T09:31:51Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e1b06b1d73cf44988c5e720c292880c02023-05-25T23:00:50ZengIEEEIEEE Access2169-35362023-01-0111492054921510.1109/ACCESS.2023.327173010113221A Unified Framework for Graph-Based Multi-View Partial Multi-Label LearningJiazheng Yuan0https://orcid.org/0000-0002-6579-2286Wei Liu1Zhibin Gu2Songhe Feng3https://orcid.org/0000-0002-5922-9358College of Science and Technology, Beijing Open University, Beijing, ChinaSchool of Computer and Information Technology, Beijing Jiaotong University, Beijing, ChinaSchool of Computer and Information Technology, Beijing Jiaotong University, Beijing, ChinaSchool of Computer and Information Technology, Beijing Jiaotong University, Beijing, ChinaMulti-view partial multi-label learning (MVPML) is a fundenmental problem where each sample is linked to multiple kinds of features and candidate labels, including ground-truth and noise labels. The key problem of MVPML is how to manipulate the multiple features and recover the ground-truth labels from candidate label set. To this end, this study designs a novel Graph-based Multi-view Partial Multi-label model named as GMPM, which combines the multi-view information detection, valuable label selection and multi-label predictor model learning into a unified optimization model. To be specific, GMPM first exploits the consensus information across multiple views by learning the view-specific similarity graph and fuses multiple graphs into a target one. Then, we divide the observed label set into two parts: the ground-truth part and the noise part, where the latter is associated with a sparse constraint to make sure the former is clean. Furthermore, we embed the learned unified similarity graph into the process of label disambiguation to restore a more reliable ground-truth label matrix. Finally, the resulting multi-label predictive model is learned with the help of ground-truth label matrix. Extensive experiments on six common used datasets demonstrate that the proposed GMPM achieves comparable performance over the state-of-the-arts.https://ieeexplore.ieee.org/document/10113221/Multi-view learningpartial multi-label learninggraph learninglow-rank and sparse decomposition |
| spellingShingle | Jiazheng Yuan Wei Liu Zhibin Gu Songhe Feng A Unified Framework for Graph-Based Multi-View Partial Multi-Label Learning IEEE Access Multi-view learning partial multi-label learning graph learning low-rank and sparse decomposition |
| title | A Unified Framework for Graph-Based Multi-View Partial Multi-Label Learning |
| title_full | A Unified Framework for Graph-Based Multi-View Partial Multi-Label Learning |
| title_fullStr | A Unified Framework for Graph-Based Multi-View Partial Multi-Label Learning |
| title_full_unstemmed | A Unified Framework for Graph-Based Multi-View Partial Multi-Label Learning |
| title_short | A Unified Framework for Graph-Based Multi-View Partial Multi-Label Learning |
| title_sort | unified framework for graph based multi view partial multi label learning |
| topic | Multi-view learning partial multi-label learning graph learning low-rank and sparse decomposition |
| url | https://ieeexplore.ieee.org/document/10113221/ |
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