Weak-label-based global and local multi-view multi-label learning with three-way clustering-Reference-Cited by-同舟云学术

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Weak-label-based global and local multi-view multi-label learning with three-way clustering

Published:2021-10-23 Issue: Volume: Page:
ISSN:1868-8071
Container-title:International Journal of Machine Learning and Cybernetics
language:en
Short-container-title:Int. J. Mach. Learn. & Cyber.

Author:

Zhu Changming,Cao Dujuan,Guo Shuaiping,Zhou Rigui,Wei Lai,Dong YiLing,Miao Duoqian

Funder

Chenguang Program

Postdoctoral Research Foundation of China

Young Scientists Fund

Natural Science Foundation of Shanghai

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

https://link.springer.com/content/pdf/10.1007/s13042-021-01450-1.pdf

Reference49 articles.

1. Liu JL, Teng SH, Fei LK, Zhang W, Fang XZ, Zhang ZX, Wu NQ (2021) A novel consensus learning approach to incomplete multi-view clustering. Pattern Recognit 115:107890

2. Tarekegn A, Giacobini M, Michalak K (2021) A review of methods for imbalanced multi-label classification. Pattern Recognit. https://doi.org/10.1016/j.patcog.2021.107965

3. Hu SZ, Yan XQ, Ye YD (2020) Dynamic auto-weighted multi-view co-clustering. Pattern Recognit 99:107101

4. Yu GX, Xing YY, Wang J, Domeniconi C, Zhang XL (2021) Multiview multi-instance multilabel active learning. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2021.3056436

5. Zhu Y, Kwok JT, Zhou ZH (2017) Multi-label learning with global and local label correlation. IEEE Trans Knowl Data Eng 99:1–24

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3. Selective label enhancement for multi-label classification based on three-way decisions;International Journal of Approximate Reasoning;2022-11

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