2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDSPTM-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
SessionSPTM-13: Models, Methods and Algorithms 1
LocationGather.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
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.