Multi-label feature selection based on dynamic graph Laplacian
In view of the problems that graph-based multi-label feature selection methods ignore the dynamic change of graph Laplacian matrix, as well as such methods employ logical-value labels to guide feature selection process and loses label information, a multi-label feature selection method based on both...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2020-12-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436X.2020244/ |
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| _version_ | 1841539277699153920 |
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| author | Yonghao LI Liang HU Ping ZHANG Wanfu GAO |
| author_facet | Yonghao LI Liang HU Ping ZHANG Wanfu GAO |
| author_sort | Yonghao LI |
| collection | DOAJ |
| description | In view of the problems that graph-based multi-label feature selection methods ignore the dynamic change of graph Laplacian matrix, as well as such methods employ logical-value labels to guide feature selection process and loses label information, a multi-label feature selection method based on both dynamic graph Laplacian matrix and real-value labels was proposed.The robust low-dimensional space of feature matrix was used to construct a dynamic graph Laplacian matrix, and the robust low-dimensional space was used as the real-value label space.Furthermore, manifold and non-negative constraints were adopted to transform logical labels into real-valued labels to address the issues mentioned above.The proposed method was compared to three multi-label feature selection methods on nine multi-label benchmark data sets in experiments.The experimental results demonstrate that the proposed multi-label feature selection method can obtain the higher quality feature subset and achieve good classification performance. |
| format | Article |
| id | doaj-art-5c5b8efd42074767b946c987534d29b5 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2020-12-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-5c5b8efd42074767b946c987534d29b52025-01-14T07:21:16ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2020-12-0141475959738941Multi-label feature selection based on dynamic graph LaplacianYonghao LILiang HUPing ZHANGWanfu GAOIn view of the problems that graph-based multi-label feature selection methods ignore the dynamic change of graph Laplacian matrix, as well as such methods employ logical-value labels to guide feature selection process and loses label information, a multi-label feature selection method based on both dynamic graph Laplacian matrix and real-value labels was proposed.The robust low-dimensional space of feature matrix was used to construct a dynamic graph Laplacian matrix, and the robust low-dimensional space was used as the real-value label space.Furthermore, manifold and non-negative constraints were adopted to transform logical labels into real-valued labels to address the issues mentioned above.The proposed method was compared to three multi-label feature selection methods on nine multi-label benchmark data sets in experiments.The experimental results demonstrate that the proposed multi-label feature selection method can obtain the higher quality feature subset and achieve good classification performance.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436X.2020244/multi-label feature selectiondynamic graph Laplacian matrixreal-value labelclassification |
| spellingShingle | Yonghao LI Liang HU Ping ZHANG Wanfu GAO Multi-label feature selection based on dynamic graph Laplacian Tongxin xuebao multi-label feature selection dynamic graph Laplacian matrix real-value label classification |
| title | Multi-label feature selection based on dynamic graph Laplacian |
| title_full | Multi-label feature selection based on dynamic graph Laplacian |
| title_fullStr | Multi-label feature selection based on dynamic graph Laplacian |
| title_full_unstemmed | Multi-label feature selection based on dynamic graph Laplacian |
| title_short | Multi-label feature selection based on dynamic graph Laplacian |
| title_sort | multi label feature selection based on dynamic graph laplacian |
| topic | multi-label feature selection dynamic graph Laplacian matrix real-value label classification |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436X.2020244/ |
| work_keys_str_mv | AT yonghaoli multilabelfeatureselectionbasedondynamicgraphlaplacian AT lianghu multilabelfeatureselectionbasedondynamicgraphlaplacian AT pingzhang multilabelfeatureselectionbasedondynamicgraphlaplacian AT wanfugao multilabelfeatureselectionbasedondynamicgraphlaplacian |