Learning Combination of Graph Filters for Graph Signal Modeling - Archive ouverte HAL Access content directly
Journal Articles IEEE Signal Processing Letters Year : 2019

Learning Combination of Graph Filters for Graph Signal Modeling

(1) , (2) , (1) , (1) , (3) , (4)
1
2
3
4

Abstract

We study the problem of parametric modeling of network-structured signals with graph filters. To benefit from the properties of several graph shift operators simultaneously, and to enhance interpretability, we investigate combinations of parallel graph filters with different shift operators. Due to their extra degrees of freedom, these models might suffer from over-fitting. We address this problem through a weighted ℓ 2 -norm regularization formulation to perform model selection by encouraging group sparsity. What makes this formulation interesting is that it is actually a smooth convex optimization problem. Experiments on real-world data structured by undirected and directed graphs show the effectiveness of this method.
Fichier principal
Vignette du fichier
Convex_combination_of_graph_filters.pdf (371.67 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-02367868 , version 1 (14-01-2020)

Identifiers

  • HAL Id : hal-02367868 , version 1

Cite

Fei Hua, Cédric Richard, Chen Jie, Haiyan Wang, Pierre Borgnat, et al.. Learning Combination of Graph Filters for Graph Signal Modeling. IEEE Signal Processing Letters, 2019, 26 (12), pp.1912-1916. ⟨hal-02367868⟩
153 View
223 Download

Share

Gmail Facebook Twitter LinkedIn More