论文标题
具有嘈杂功能和不平衡标签的多视图弱标签学习
Incomplete Multi-View Weak-Label Learning with Noisy Features and Imbalanced Labels
论文作者
论文摘要
各种现代应用都展示了多视标的多标签学习，每个样本都具有多视图功能，并且多个标签通过共同视图相关。当前方法通常无法直接处理每个样本只观察到特征和标签子集的设置，并且忽略了现实世界中嘈杂的视图和不平衡标签的存在。在本文中，我们提出了一种克服局限性的新方法。它将不完整的视图和弱标签嵌入具有适应性重量的低维子空间中，并通过自动加权的Hilbert-Schmidt独立标准（HSIC）促进了嵌入重量矩阵之间的差异，以减少冗余。此外，它可以自适应地学习嵌入噪声观点的意义，并通过局灶性损失来减轻标签失衡问题。对四个现实世界多视频标签数据集的实验结果证明了该方法的有效性。
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.
