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.

翻译：现代应用中出现多种多视图多标签学习场景，其中每个样本具有多视图特征，且多个标签通过共同视图相互关联。现有方法通常无法直接处理每个样本仅观测到部分特征和标签的情况，且忽略了现实问题中噪声视图与标签不平衡的存在。本文提出一种新方法以克服上述局限。该方法通过自适应权重将不完整视图与弱标签联合嵌入低维子空间，并利用自加权希尔伯特-施密特独立性准则（HSIC）促进嵌入权重矩阵间的差异性以降低冗余。此外，该方法能自适应学习视图重要性权重以检测噪声视图，并通过焦点损失函数缓解标签不平衡问题。在四个真实多视图多标签数据集上的实验结果证明了该方法的有效性。