Learning Combination of Graph Filters for Graph Signal Modeling

Fei Hua, Cedric Richard, Jie Chen, Haiyan Wang, Pierre Borgnat, Paulo Goncalves

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

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 $\ell _2$-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.

Original languageEnglish
Article number8908677
Pages (from-to)1912-1916
Number of pages5
JournalIEEE Signal Processing Letters
Volume26
Issue number12
DOIs
StatePublished - Dec 2019

Keywords

  • graph signal processing
  • group sparsity
  • model selection
  • parallel graph filters
  • Parametric modeling

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