Sorry, you need to enable JavaScript to visit this website.

facebooktwittermailshare

Convolutional Neural Networks via Node-Varying Graph Filters

Abstract: 

Convolutional neural networks (CNNs) are being applied to an increasing number of problems and fields due to their superior performance in classification and regression tasks. Since two of the key operations that CNNs implement are convolution and pooling, this type of networks is implicitly designed to act on data described by regular structures such as images. Motivated by the recent interest in processing signals defined in irregular domains, we advocate a CNN architecture that operates on signals supported on graphs. The proposed design replaces the classical convolution not with a node-invariant graph filter (GF), which is the natural generalization of convolution to graph domains, but with a node-varying GF. This filter extracts different local features without increasing the output dimension of each layer and, as a result, bypasses the need for a pooling stage while involving only local operations. A second contribution is to replace the node-varying GF with a hybrid node-varying GF, which is a new type of GF introduced in this paper. While the alternative architecture can still be run locally without requiring a pooling stage, the number of trainable parameters is smaller and can be rendered independent of the data dimension. Tests are run on a synthetic source localization problem and on the 20NEWS dataset.

up
0 users have voted:

Paper Details

Authors:
Fernando Gama, Geert Leus, Antonio Marques, Alejandro Ribeiro
Submitted On:
31 May 2018 - 7:03pm
Short Link:
Type:
Presentation Slides
Event:
Presenter's Name:
Fernando Gama
Paper Code:
1064
Document Year:
2018
Cite

Document Files

gama-leus-marques-ribeiro-node_variant_graph_filter.pdf

(93 downloads)

Subscribe

[1] Fernando Gama, Geert Leus, Antonio Marques, Alejandro Ribeiro, "Convolutional Neural Networks via Node-Varying Graph Filters", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3226. Accessed: Dec. 10, 2018.
@article{3226-18,
url = {http://sigport.org/3226},
author = {Fernando Gama; Geert Leus; Antonio Marques; Alejandro Ribeiro },
publisher = {IEEE SigPort},
title = {Convolutional Neural Networks via Node-Varying Graph Filters},
year = {2018} }
TY - EJOUR
T1 - Convolutional Neural Networks via Node-Varying Graph Filters
AU - Fernando Gama; Geert Leus; Antonio Marques; Alejandro Ribeiro
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3226
ER -
Fernando Gama, Geert Leus, Antonio Marques, Alejandro Ribeiro. (2018). Convolutional Neural Networks via Node-Varying Graph Filters. IEEE SigPort. http://sigport.org/3226
Fernando Gama, Geert Leus, Antonio Marques, Alejandro Ribeiro, 2018. Convolutional Neural Networks via Node-Varying Graph Filters. Available at: http://sigport.org/3226.
Fernando Gama, Geert Leus, Antonio Marques, Alejandro Ribeiro. (2018). "Convolutional Neural Networks via Node-Varying Graph Filters." Web.
1. Fernando Gama, Geert Leus, Antonio Marques, Alejandro Ribeiro. Convolutional Neural Networks via Node-Varying Graph Filters [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3226