We present a novel approach for multiview canonical correlation analysis based on a variational graph neural network model. We propose a nonlinear model which takes into account the available graph-based geometric constraints while being scalable to large-scale datasets with multiple views. This model combines the probabilistic interpretation of CCA with an autoencoder architecture based on graph convolutional neural network layers. Experiments with the proposed method are conducted on classification, clustering, and recommendation tasks on real datasets. The algorithm is competitive with state-of-the-art multiview representation learning techniques, in addition to being scalable and robust to instances with missing views.

我们提出了一种基于变分图神经网络模型的多视图典型相关分析的新方法。我们提出了一种非线性模型，该模型考虑了可用的基于图形的几何约束，同时可扩展到具有多个视图的大规模数据集。该模型将 CCA 的概率解释与基于图卷积神经网络层的自动编码器架构相结合。使用所提出的方法对真实数据集上的分类、聚类和推荐任务进行了实验。除了对缺少视图的实例具有可扩展性和鲁棒性之外，该算法还可以与最先进的多视图表示学习技术竞争。