Incomplete Multi-view Weak-Label Learning with Noisy Features and Imbalanced Labels

doi:10.1007/978-981-99-7022-3_12

	Incomplete Multi-view Weak-Label Learning with Noisy Features and Imbalanced Labels
	Li, Zhiwei1 ; Yang, Zijian1 ; Sun, Lu1 ; Kudo, Mineichi 2; Kimura, Keigo 2
	2024
会议录名称	LECTURE NOTES IN COMPUTER SCIENCE (INCLUDING SUBSERIES LECTURE NOTES IN ARTIFICIAL INTELLIGENCE AND LECTURE NOTES IN BIOINFORMATICS)
ISSN	0302-9743
卷号	14326 LNAI
页码	124-130
DOI	10.1007/978-981-99-7022-3_12
摘要	A variety of modern applications exhibit multi-view multi-label learning, where each sample has multi-view features, and multiple labels are correlated via common views. Current methods usually fail to directly deal with the setting where only a subset of features and labels are observed for each sample, and ignore the presence of noisy views and imbalanced labels in real-world problems. In this paper, we propose a novel method to overcome the limitations. It jointly embeds incomplete views and weak labels into a low-dimensional subspace with adaptive weights, and facilitates the difference between embedding weight matrices via auto-weighted Hilbert-Schmidt Independence Criterion (HSIC) to reduce the redundancy. Moreover, it adaptively learns view-wise importance for embedding to detect noisy views, and mitigates the label imbalance problem by focal loss. Experimental results on four real-world multi-view multi-label datasets demonstrate the effectiveness of the proposed method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.
关键词	Embeddings Focal loss Hilbert-schmidt independence criterions Modern applications Multi-label learning Multi-view multi-label learning Multi-views Multiple labels Weak labels Weakly supervised learning
会议名称	20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023
会议地点	Jakarta, Indonesia
会议日期	November 15, 2023 - November 19, 2023
URL	查看原文
收录类别	EI
语种	英语
出版者	Springer Science and Business Media Deutschland GmbH
EI入藏号	20234715098386
EI主题词	Embeddings
EISSN	1611-3349
EI分类号	723.4 Artificial Intelligence
原始文献类型	Conference article (CA)
文献类型	会议论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/348704
专题	信息科学与技术学院_硕士生信息科学与技术学院_PI研究组_孙露组
通讯作者	Yang, Zijian
作者单位	1.ShanghaiTech University, 393 Middle Huaxia Road, Shanghai, Pudong, China 2.Hokkaido University, Kita 8, Nishi 5, Kita-ku, Hokkaido, Sapporo, Japan
第一作者单位	上海科技大学
通讯作者单位	上海科技大学
第一作者的第一单位	上海科技大学
推荐引用方式 GB/T 7714	Li, Zhiwei,Yang, Zijian,Sun, Lu,et al. Incomplete Multi-view Weak-Label Learning with Noisy Features and Imbalanced Labels[C]:Springer Science and Business Media Deutschland GmbH,2024:124-130.