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Simultaneous graph learning and blind separation of graph signal sources

Einizade, A ; Sharif University of Technology | 2021

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  1. Type of Document: Article
  2. DOI: 10.1109/LSP.2021.3093872
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2021
  4. Abstract:
  5. When our sources are graph signals, a more efficient algorithm for Blind Source Separation (BSS) can be provided by using structural graph information along with statistical independence and/or non-Gaussianity. To the best of our knowledge, the GraphJADE and GraDe algorithms are the only BSS methods addressing this issue in the case of known underlying graphs. However, in many real-world applications, these graphs are not necessarily a priori known. In this paper, we propose a method called GraphJADE-GL (GraphJADE with Graph Learning) that jointly separates the graph signal sources and learns the graphs related to them accurately, in an alternating style. © 1994-2012 IEEE
  6. Keywords:
  7. Graph algorithms ; Blind separation ; Nongaussianity ; Real-world ; Signal source ; Statistical independence ; Structural graph ; Underlying graphs ; Blind source separation
  8. Source: IEEE Signal Processing Letters ; Volume 28 , 2021 , Pages 1495-1499 ; 10709908 (ISSN)
  9. URL: https://ieeexplore.ieee.org/document/9468906