论文标题
用于规范相关分析的多视变量自动编码器
Multiview Variational Graph Autoencoders for Canonical Correlation Analysis
论文作者
论文摘要
我们提出了一种基于变异方法的新型多视量规范相关分析模型。这是第一个考虑可用基于图的几何约束的非线性模型，同时可扩展使用具有多个视图的大型数据集。它基于具有图形卷积神经网络层的自动编码器体系结构。我们在实际数据集上实验了有关分类，聚类和推荐任务的方法。该算法具有最先进的多视图表示技术的竞争。
We present a novel multiview canonical correlation analysis model based on a variational approach. This is the first nonlinear model that takes into account the available graph-based geometric constraints while being scalable for processing large scale datasets with multiple views. It is based on an autoencoder architecture with graph convolutional neural network layers. We experiment with our approach on classification, clustering, and recommendation tasks on real datasets. The algorithm is competitive with state-of-the-art multiview representation learning techniques.
