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

• URL: http://arxiv.org/abs/2201.01079v5
• Date: Tue, 29 Aug 2023 08:10:29 GMT
• Title: Incomplete Multi-View Weak-Label Learning with Noisy Features and Imbalanced Labels
• Authors: Zhiwei Li, Zijian Yang, Lu Sun, Mineichi Kudo, Kego Kimura
• Abstract summary: We propose a novel method to overcome the limitations of multi-view learning. It embeds incomplete views and weak labels into a low-dimensional subspace with adaptive weights. It adaptively learns view-wise importance for embedding to detect noisy views, and mitigates the label imbalance problem by focal loss.
• Score: 4.800187500079582
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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