Graph Signal Processing Meets Blind Source Separation

Research output: Contribution to journalArticleScientificpeer-review

Abstract

In graph signal processing (GSP), prior information on the dependencies in the signal is collected in a graph which is then used when processing or analyzing the signal. Blind source separation (BSS) techniques have been developed and analyzed in different domains, but for graph signals the research on BSS is still in its infancy. In this paper, this gap is filled with two contributions. First, a nonparametric BSS method, which is relevant to the GSP framework, is refined, the Cramér-Rao bound (CRB) for mixing and unmixing matrix estimators in the case of Gaussian moving average graph signals is derived, and for studying the achievability of the CRB, a new parametric method for BSS of Gaussian moving average graph signals is introduced. Second, we also consider BSS of non-Gaussian graph signals and two methods are proposed. Identifiability conditions show that utilizing both graph structure and non-Gaussianity provides a more robust approach than methods which are based on only either graph dependencies or non-Gaussianity. It is also demonstrated by numerical study that the proposed methods are more efficient in separating non-Gaussian graph signals.

Original languageEnglish
Article number9405408
Pages (from-to)2585-2599
Number of pages15
JournalIEEE Transactions on Signal Processing
Volume69
Early online date2021
DOIs
Publication statusPublished - 2021
MoE publication typeA1 Journal article-refereed

Keywords

  • Adjacency matrix
  • Approximate joint diagonalization
  • Blind source separation
  • Covariance matrices
  • Cramer-Rao bound
  • Decorrelation
  • Graph moving average model
  • Independent component analysis
  • Integrated circuits
  • Linear matrix inequalities
  • Random variables
  • Symmetric matrices

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