Label distribution learning by utilizing common and label-specific feature fusion space-Reference-Cited by-同舟云学术

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Label distribution learning by utilizing common and label-specific feature fusion space

Published:2024-09-04 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:

Zhang Ziyun,Wang Jing,Geng Xin

Funder

National Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province

China Postdoctoral Science Foundation

Jiangsu Funding Program for Excellent Postdoctoral Talent

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s13042-024-02351-9.pdf

Reference50 articles.

1. Geng X (2016) Label distribution learning. IEEE Trans Knowl Data Eng 28(7):1734–1748

2. Rupprecht C, Laina I, DiPietro R, Baust M, Tombari F, Navab N, Hager GD (2017) Learning in an uncertain world: representing ambiguity through multiple hypotheses. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3591–3600

3. Tsoumakas G, Katakis I (2007) Multi-label classification: an overview. Int J Data Warehous Min 3(3):1–13

4. Liu C, Wen J, Luo X, Huang C, Wu Z, Xu Y (2023) Dicnet: Deep instance-level contrastive network for double incomplete multi-view multi-label classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 8807–8815

5. Liu C, Wen J, Liu Y, Huang C, Wu Z, Luo X, Xu Y (2024) Masked two-channel decoupling framework for incomplete multi-view weak multi-label learning. In: Proceedings of the 37th Conference on Neural Information Processing Systems, pp. 1–14

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