Multiview Variational Graph Autoencoders for Canonical Correlation Analysis

10/30/2020
by   Yacouba Kaloga, et al.
0

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.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset