CNN architectures for GRAPH data

Conference paper (2019)

Authors

F. Gama University of Pennsylvania

Antonio G. Marques King Juan Carlos University

G.J.T. Leus Signal Processing Systems -

Alejandro Ribeiro University of Pennsylvania

Research Group

Signal Processing Systems () (TU Delft)

DOI: https://doi.org/10.1109/GlobalSIP.2018.8646348

Deep learning Graph signal processing Convolutional neural networks Geometric learning

To reference this document use:

http://resolver.tudelft.nl/uuid:add3cb34-1a6d-4271-8502-e879608878ad

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Published Date

2019

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Signal Processing Systems

Abstract

In this ongoing work, we describe several architectures that generalize convolutional neural networks (CNNs) to process signals supported on graphs. The general idea of the replace time invariant filters with graph filters to generate convolutional features and to replace pooling with sampling schemes for graph signals. The different architectures are compared and the key trade offs are identified. Numerical simulations with both synthetic and real-world data are used to illustrate the advantages of the proposed approaches.

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