Multiview Variational Graph Autoencoders for Canonical Correlation Analysis

Yacouba Kaloga; Pierre Borgnat; Sundeep Prabhakar Chepuri; Patrice Abry; Amaury Habrard

ICASSP 2021accepted0 citations

Multiview Variational Graph Autoencoders for Canonical Correlation Analysis

Yacouba Kaloga, Pierre Borgnat, Sundeep Prabhakar Chepuri, Patrice Abry, Amaury Habrard

Abstract

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.

BibTeX

@inproceedings{icassp2021_multiviewvariati,
  title = {Multiview Variational Graph Autoencoders for Canonical Correlation Analysis},
  author = {Yacouba Kaloga and Pierre Borgnat and Sundeep Prabhakar Chepuri and Patrice Abry and Amaury Habrard},
  booktitle = {ICASSP 2021},
  year = {2021}
}