Fan, Yufan ; Trinh-Hoang, Minh ; Pesavento, Marius (2021)
Decentralized Eigendecomposition for Online Learning over Graphs.
29th European Signal Processing Conference (EUSIPCO). Dublin, Ireland (23.08.2021-27.08.2021)
doi: 10.23919/EUSIPCO54536.2021.9616034
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
We address the problem of decentralized eigenvalue decomposition of a general symmetric matrix that is important, e.g., in Principal Component Analysis. The proposed algorithm only uses local interactions among neighboring agents and is based on the representation of the matrix as a recursive update of rank-one components. This makes the algorithm attractive for online eigenvalue and eigenvector tracking applications. We study the performance of the proposed algorithm in two important application examples: First, we consider the online eigendecomposition of a sample covariance matrix over the network. Then, we investigate the online computation of the spectra of the graph Laplacian that is important in, e.g., graph Fourier analysis and graph-dependent filter design. Simulation results reveal that the proposed algorithm outperforms existing decentralized algorithms both in terms of estimation accuracy as well as communication cost.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2021 |
| Autor(en): | Fan, Yufan ; Trinh-Hoang, Minh ; Pesavento, Marius |
| Art des Eintrags: | Bibliographie |
| Titel: | Decentralized Eigendecomposition for Online Learning over Graphs |
| Sprache: | Englisch |
| Publikationsjahr: | 8 Dezember 2021 |
| Verlag: | IEEE |
| Buchtitel: | 29th European Signal Processing Conference (EUSIPCO 2021): Proceedings |
| Veranstaltungstitel: | 29th European Signal Processing Conference (EUSIPCO) |
| Veranstaltungsort: | Dublin, Ireland |
| Veranstaltungsdatum: | 23.08.2021-27.08.2021 |
| DOI: | 10.23919/EUSIPCO54536.2021.9616034 |
| Kurzbeschreibung (Abstract): | We address the problem of decentralized eigenvalue decomposition of a general symmetric matrix that is important, e.g., in Principal Component Analysis. The proposed algorithm only uses local interactions among neighboring agents and is based on the representation of the matrix as a recursive update of rank-one components. This makes the algorithm attractive for online eigenvalue and eigenvector tracking applications. We study the performance of the proposed algorithm in two important application examples: First, we consider the online eigendecomposition of a sample covariance matrix over the network. Then, we investigate the online computation of the spectra of the graph Laplacian that is important in, e.g., graph Fourier analysis and graph-dependent filter design. Simulation results reveal that the proposed algorithm outperforms existing decentralized algorithms both in terms of estimation accuracy as well as communication cost. |
| Fachbereich(e)/-gebiet(e): | 18 Fachbereich Elektrotechnik und Informationstechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik 18 Fachbereich Elektrotechnik und Informationstechnik > Institut für Nachrichtentechnik > Nachrichtentechnische Systeme |
| Hinterlegungsdatum: | 06 Mär 2023 11:15 |
| Letzte Änderung: | 12 Jul 2023 15:04 |
| PPN: | 509537847 |
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