Robust feature selection method via joint low-rank reconstruction and projection reconstruction
Aiming at the problem that current feature selection methods were still affected by noise and cannot effectively unify clustering and reconstruction effects, a robust feature selection method was proposed.A robust reconstruction error term was built by making the difference between low-rank reconstr...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Journal on Communications
2023-03-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023061/ |
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| _version_ | 1841540013386366976 |
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| author | Shuangyan YI Yongsheng LIANG Jingjing LU Wei LIU Tao HU Zhenyu HE |
| author_facet | Shuangyan YI Yongsheng LIANG Jingjing LU Wei LIU Tao HU Zhenyu HE |
| author_sort | Shuangyan YI |
| collection | DOAJ |
| description | Aiming at the problem that current feature selection methods were still affected by noise and cannot effectively unify clustering and reconstruction effects, a robust feature selection method was proposed.A robust reconstruction error term was built by making the difference between low-rank reconstruction and projection reconstruction.After that, the features for clustering were selected from the reconstructed data instead of the original data.The learning of clean data and feature selection technique are allowed for joint learning and promote each other, thereby improving the robustness of the method on noisy data, and effectively unifying reconstruction and clustering.Compared with several kinds of graph embedding feature selection and reconstruction feature selection methods on five datasets, the experimental results showed that, except for the LUNG noise dataset, the proposed method outperforms the comparative feature selection method under both evaluation indicators (ACC and NMI). |
| format | Article |
| id | doaj-art-0212220be5ac4758b1989c4faced05b5 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2023-03-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-0212220be5ac4758b1989c4faced05b52025-01-14T06:23:26ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2023-03-014420921959387992Robust feature selection method via joint low-rank reconstruction and projection reconstructionShuangyan YIYongsheng LIANGJingjing LUWei LIUTao HUZhenyu HEAiming at the problem that current feature selection methods were still affected by noise and cannot effectively unify clustering and reconstruction effects, a robust feature selection method was proposed.A robust reconstruction error term was built by making the difference between low-rank reconstruction and projection reconstruction.After that, the features for clustering were selected from the reconstructed data instead of the original data.The learning of clean data and feature selection technique are allowed for joint learning and promote each other, thereby improving the robustness of the method on noisy data, and effectively unifying reconstruction and clustering.Compared with several kinds of graph embedding feature selection and reconstruction feature selection methods on five datasets, the experimental results showed that, except for the LUNG noise dataset, the proposed method outperforms the comparative feature selection method under both evaluation indicators (ACC and NMI).http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023061/reconstructionlow-rankprojectionsparsityfeature selection |
| spellingShingle | Shuangyan YI Yongsheng LIANG Jingjing LU Wei LIU Tao HU Zhenyu HE Robust feature selection method via joint low-rank reconstruction and projection reconstruction Tongxin xuebao reconstruction low-rank projection sparsity feature selection |
| title | Robust feature selection method via joint low-rank reconstruction and projection reconstruction |
| title_full | Robust feature selection method via joint low-rank reconstruction and projection reconstruction |
| title_fullStr | Robust feature selection method via joint low-rank reconstruction and projection reconstruction |
| title_full_unstemmed | Robust feature selection method via joint low-rank reconstruction and projection reconstruction |
| title_short | Robust feature selection method via joint low-rank reconstruction and projection reconstruction |
| title_sort | robust feature selection method via joint low rank reconstruction and projection reconstruction |
| topic | reconstruction low-rank projection sparsity feature selection |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2023061/ |
| work_keys_str_mv | AT shuangyanyi robustfeatureselectionmethodviajointlowrankreconstructionandprojectionreconstruction AT yongshengliang robustfeatureselectionmethodviajointlowrankreconstructionandprojectionreconstruction AT jingjinglu robustfeatureselectionmethodviajointlowrankreconstructionandprojectionreconstruction AT weiliu robustfeatureselectionmethodviajointlowrankreconstructionandprojectionreconstruction AT taohu robustfeatureselectionmethodviajointlowrankreconstructionandprojectionreconstruction AT zhenyuhe robustfeatureselectionmethodviajointlowrankreconstructionandprojectionreconstruction |