| Paper ID | SPTM-13.4 |
| Paper Title |
MULTIVIEW VARIATIONAL GRAPH AUTOENCODERS FOR CANONICAL CORRELATION ANALYSIS |
| Authors |
Yacouba Kaloga, Pierre Borgnat, ENS de LYON, France; Sundeep Prabhakar Chepuri, Indian Institute of Science, India; Patrice Abry, ENS de Lyon, France; Amaury Habrard, Universite Jean Monnet de Saint-Etienne, France |
| Session | SPTM-13: Models, Methods and Algorithms 1 |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 13:00 - 13:45 |
| Presentation Time: | Thursday, 10 June, 13:00 - 13:45 |
| Presentation |
Poster
|
| Topic |
Signal Processing Theory and Methods: [SSP] Statistical Signal Processing |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
We present a novel Multiview Canonical Correlation Analysis model based on a variational approach. This is the first non linear model able to take into account some a priori graph- based geometric constraints while being scalable for process- ing large scale datasets with multiple views. It is based on an autoencoder architecture making use of Graph Convolu- tional Neural network models. We experiment our approach on classification, clustering and recommendation tasks. The algorithm is competitive among multiview models taking ac- count geometric information while remaining more scalable. |
