Robust Label and Feature Space Co-Learning for Multi-Label Classification
Multi-label classification remains a challenging task for high-dimensional data samples and their labels both increase the complexity of training models. In this paper, we propose a Robust Label and Feature Space Co-Learning method, referred to as RLFSCL, for multi-label classification. Different fr...
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| Veröffentlicht in: | IEEE transactions on knowledge and data engineering 2023-11, Vol.35 (11), p.1-14 |
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| container_title | IEEE transactions on knowledge and data engineering |
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| creator | Liu, Zhifeng Tang, Chuanjing Abhadiomhen, Stanley Ebhohimhen Shen, Xiang-Jun Li, Yangyang |
| description | Multi-label classification remains a challenging task for high-dimensional data samples and their labels both increase the complexity of training models. In this paper, we propose a Robust Label and Feature Space Co-Learning method, referred to as RLFSCL, for multi-label classification. Different from traditional multi-label classification methods which focus on feature space learning through regression directly between data samples and labels, our proposed method can further learn robust low rank label space from this traditional regression method. Therefore, our RLFSCL can learn better low rank feature and label representations simultaneously in original noisy and high dimensional spaces. Experimental comparison on five benchmark datasets, including Rcv1s5, Cal500, and Corel16k4 shows that the proposed RLFSCL algorithm outperforms state-of-the-art multi-label classification methods. The code of RLFSCL is made available on https://github.com/JingChuanTang/RLFSCL. |
| doi_str_mv | 10.1109/TKDE.2022.3232114 |
| format | Article |
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In this paper, we propose a Robust Label and Feature Space Co-Learning method, referred to as RLFSCL, for multi-label classification. Different from traditional multi-label classification methods which focus on feature space learning through regression directly between data samples and labels, our proposed method can further learn robust low rank label space from this traditional regression method. Therefore, our RLFSCL can learn better low rank feature and label representations simultaneously in original noisy and high dimensional spaces. Experimental comparison on five benchmark datasets, including Rcv1s5, Cal500, and Corel16k4 shows that the proposed RLFSCL algorithm outperforms state-of-the-art multi-label classification methods. 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(IEEE) 2023</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c294t-5a42fb2ca92a2fbbca3323fe4f59f6e09deea0328b23f98ebb7f0ae6afef915c3</citedby><cites>FETCH-LOGICAL-c294t-5a42fb2ca92a2fbbca3323fe4f59f6e09deea0328b23f98ebb7f0ae6afef915c3</cites><orcidid>0000-0002-9805-233X ; 0000-0002-3359-8972 ; 0000-0002-4664-1140 ; 0000-0002-9509-1915</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10004974$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>315,781,785,797,27928,27929,54762</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/10004974$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Liu, Zhifeng</creatorcontrib><creatorcontrib>Tang, Chuanjing</creatorcontrib><creatorcontrib>Abhadiomhen, Stanley Ebhohimhen</creatorcontrib><creatorcontrib>Shen, Xiang-Jun</creatorcontrib><creatorcontrib>Li, Yangyang</creatorcontrib><title>Robust Label and Feature Space Co-Learning for Multi-Label Classification</title><title>IEEE transactions on knowledge and data engineering</title><addtitle>TKDE</addtitle><description>Multi-label classification remains a challenging task for high-dimensional data samples and their labels both increase the complexity of training models. 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| subjects | Adaptation models Algorithms Classification Classification algorithms Correlation Feature extraction feature space label space Labels Learning low-rank learning multi-label classification Noise measurement noise reduction Representation learning Robustness (mathematics) Training |
| title | Robust Label and Feature Space Co-Learning for Multi-Label Classification |
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