Estimation of Time-Varying Graph Signals by Learning Graph Dictionaries Zamanda Deǧişen Graf Sinyallerinin Kestirimi için Graflarda Sözlük Öǧrenme

Acar A. B., VURAL E.

30th Signal Processing and Communications Applications Conference, SIU 2022, Safranbolu, Türkiye, 15 - 18 Mayıs 2022 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/siu55565.2022.9864704
  • Basıldığı Şehir: Safranbolu
  • Basıldığı Ülke: Türkiye
  • Anahtar Kelimeler: graph dictionary learning, graph kernels, Graph signal processing, time-vertex spectrum
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

© 2022 IEEE.We study the problem of estimating time-varying graph signals from missing observations. We propose a method based on learning graph dictionaries specified by a set of time-vertex kernels in the joint spectral domain. The parameters of the time-vertex kernels are optimized jointly with the sparse representation coefficients of the signals, so that the learnt representation fits well to the available observations of the time-vertex signals at hand. The missing observations of the signals are then estimated based on their reconstruction with the learnt model. Experimental results on real graph signal data sets show that the proposed method outperforms classical graph-based regression approaches.