论文标题
良好多视图学习的层次结构最佳运输
Hierarchical Optimal Transport for Robust Multi-View Learning
论文作者
论文摘要
传统的多视图学习方法通常依赖两个假设：（（$ i $）不同视图中的样本与（$ ii $）在潜在空间中的表示形式遵守相同的分布。不幸的是，这两个假设在实践中可能是值得怀疑的，这限制了多视图学习的应用。在这项工作中，我们提出了一种分层最佳传输（热）方法，以减轻对这两个假设的依赖性。给定未对齐的多视图数据，热方法对不同视图的分布之间的切片瓦斯坦距离处惩罚。这些切成薄片的Wasestein距离被用作地面距离，以计算跨不同视图的熵最佳运输，这明确表示视图的聚类结构。热方法适用于无监督和半监督的学习，实验结果表明，它在合成和现实世界任务上都可以强大。
Traditional multi-view learning methods often rely on two assumptions: ($i$) the samples in different views are well-aligned, and ($ii$) their representations in latent space obey the same distribution. Unfortunately, these two assumptions may be questionable in practice, which limits the application of multi-view learning. In this work, we propose a hierarchical optimal transport (HOT) method to mitigate the dependency on these two assumptions. Given unaligned multi-view data, the HOT method penalizes the sliced Wasserstein distance between the distributions of different views. These sliced Wasserstein distances are used as the ground distance to calculate the entropic optimal transport across different views, which explicitly indicates the clustering structure of the views. The HOT method is applicable to both unsupervised and semi-supervised learning, and experimental results show that it performs robustly on both synthetic and real-world tasks.
