论文标题
跨视图对比度表示在部分对齐的多视图数据上学习
Cross-view Graph Contrastive Representation Learning on Partially Aligned Multi-view Data
论文作者
论文摘要
在过去的几十年中，多视图表示学习已经迅速发展，并且已应用于许多领域。但是，大多数以前的作品都认为每个视图都是完整和对齐的。当遇到诸如缺失或不规则的观点之类的实际问题时，这会导致其表现不可避免的恶化。为了应对部分对齐的多视图数据的表示形式学习的挑战，我们提出了一个新的跨视图对比度学习框架，该框架集成了多视图信息以对齐数据并学习潜在表示。与当前方法相比，所提出的方法具有以下优点：（1）我们的模型是一个端到端框架，通过通过特定视图的自动编码器和群集级别的数据对齐来同时执行特定视图的表示学习，并通过将多视图信息与交叉观看图形截面的学习相结合； （2）当设计跨视图对比度学习时，很容易应用我们的模型来探索来自三种或多种模式/来源的信息。在几个真实数据集上进行的广泛实验证明了拟议方法对聚类和分类任务的有效性。
Multi-view representation learning has developed rapidly over the past decades and has been applied in many fields. However, most previous works assumed that each view is complete and aligned. This leads to an inevitable deterioration in their performance when encountering practical problems such as missing or unaligned views. To address the challenge of representation learning on partially aligned multi-view data, we propose a new cross-view graph contrastive learning framework, which integrates multi-view information to align data and learn latent representations. Compared with current approaches, the proposed method has the following merits: (1) our model is an end-to-end framework that simultaneously performs view-specific representation learning via view-specific autoencoders and cluster-level data aligning by combining multi-view information with the cross-view graph contrastive learning; (2) it is easy to apply our model to explore information from three or more modalities/sources as the cross-view graph contrastive learning is devised. Extensive experiments conducted on several real datasets demonstrate the effectiveness of the proposed method on the clustering and classification tasks.
