CONVOLUTIONAL NEURAL NETWORKS VIA NODE-VARYING GRAPH FILTERS

被引:0
|
作者
Gaina, Fernando [1 ]
Leust, Geert [2 ]
Marques, Antonio G. [3 ]
Ribeiro, Alejandro [1 ]
机构
[1] Univ Penn, Dept Elect & Syst Engn, Philadelphia, PA 19104 USA
[2] Delft Univ Technol, Dept Microelect, Delft, Netherlands
[3] King Juan Carlos Univ, Dept Signal Theory & Commun, Madrid, Spain
来源
2018 IEEE DATA SCIENCE WORKSHOP (DSW) | 2018年
关键词
Convolutional neural networks; network data; graph signal processing; node-varying graph filters; SIGNALS;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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 20 NEWS dataset.
引用
收藏
页码:220 / 224
页数:5
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