Multiview Variational Graph Autoencoders for Canonical Correlation
Analysis
- URL: http://arxiv.org/abs/2010.16132v3
- Date: Mon, 4 Oct 2021 12:39:11 GMT
- Title: Multiview Variational Graph Autoencoders for Canonical Correlation
Analysis
- Authors: Yacouba Kaloga and Pierre Borgnat and Sundeep Prabhakar Chepuri and
Patrice Abry and Amaury Habrard
- Abstract summary: 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.
It is scalable for processing large scale datasets with multiple views.
- Score: 23.30313704251483
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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