论文标题
一个简洁而有效的模型，用于不结盟不完整的多视图和缺少多标签学习
A Concise yet Effective model for Non-Aligned Incomplete Multi-view and Missing Multi-label Learning
论文作者
论文摘要
实际上，从多视图多标签数据中学习不可避免地会面临三个挑战：缺少标签，不完整的视图和不结盟的视图。现有的方法主要涉及前两个，通常需要多个假设来攻击它们，即使是最新的方法也涉及至少两个显式的超参数，因此模型选择非常困难。更粗略的是，他们将无法处理第三项挑战，更不用说共同解决这三个挑战了。在本文中，我们旨在通过仅使用一个高参数建立一个简洁而有效的模型来满足这些假设。为了减轻可用标签的不足，我们不仅利用了多种视图的共识，还利用了隐藏在多个标签中的全球和本地结构。具体而言，我们引入了一个指标矩阵，以回归形式解决前两个挑战，同时将相同的单个标签和在共同标签空间中不同视图的所有标签对齐，以应对第三个挑战。在对齐中，我们将多个标签的全球和局部结构分别为高级和低级别。随后，建立了样品数量中线性时间复杂性的有效算法。最后，即使没有视图对齐，我们的方法在五个真实数据集上具有最先进的优于最先进的方法。
In reality, learning from multi-view multi-label data inevitably confronts three challenges: missing labels, incomplete views, and non-aligned views. Existing methods mainly concern the first two and commonly need multiple assumptions to attack them, making even state-of-the-arts involve at least two explicit hyper-parameters such that model selection is quite difficult. More roughly, they will fail in handling the third challenge, let alone addressing the three jointly. In this paper, we aim at meeting these under the least assumption by building a concise yet effective model with just one hyper-parameter. To ease insufficiency of available labels, we exploit not only the consensus of multiple views but also the global and local structures hidden among multiple labels. Specifically, we introduce an indicator matrix to tackle the first two challenges in a regression form while aligning the same individual labels and all labels of different views in a common label space to battle the third challenge. In aligning, we characterize the global and local structures of multiple labels to be high-rank and low-rank, respectively. Subsequently, an efficient algorithm with linear time complexity in the number of samples is established. Finally, even without view-alignment, our method substantially outperforms state-of-the-arts with view-alignment on five real datasets.
