Fan, Yufan ; Trinh-Hoang, Minh ; Ardic, Cemil Emre ; Pesavento, Marius (2023)
Decentralized Eigendecomposition for Online Learning over Graphs with Applications.
In: IEEE Transactions on Signal and Information Processing over Networks, 9
doi: 10.1109/TSIPN.2023.3302658
Artikel, Bibliographie
Dies ist die neueste Version dieses Eintrags.
Kurzbeschreibung (Abstract)
In this article, the problem of decentralized eigenvalue decomposition of a general symmetric matrix that is important, e.g., in Principal Component Analysis, is studied, and a decentralized online learning algorithm is proposed. Instead of collecting all information in a fusion center, the proposed algorithm involves only local interactions among adjacent agents. It benefits from the representation of the matrix as a sum of rank-one components which makes the algorithm attractive for online eigenvalue and eigenvector tracking applications. We examine the performance of the proposed algorithm in two types of important application examples: First, we consider the online eigendecomposition of a sample covariance matrix over the network, with application in decentralized Direction-of-Arrival (DoA) estimation and DoA tracking applications. 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. We apply our proposed algorithm to track the spectra of the graph Laplacian in static and dynamic networks. 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: | Artikel |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Fan, Yufan ; Trinh-Hoang, Minh ; Ardic, Cemil Emre ; Pesavento, Marius |
| Art des Eintrags: | Bibliographie |
| Titel: | Decentralized Eigendecomposition for Online Learning over Graphs with Applications |
| Sprache: | Englisch |
| Publikationsjahr: | 7 August 2023 |
| Verlag: | IEEE |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | IEEE Transactions on Signal and Information Processing over Networks |
| Jahrgang/Volume einer Zeitschrift: | 9 |
| Auflage: | 2. Version |
| DOI: | 10.1109/TSIPN.2023.3302658 |
| URL / URN: | https://ieeexplore.ieee.org/document/10210076 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | In this article, the problem of decentralized eigenvalue decomposition of a general symmetric matrix that is important, e.g., in Principal Component Analysis, is studied, and a decentralized online learning algorithm is proposed. Instead of collecting all information in a fusion center, the proposed algorithm involves only local interactions among adjacent agents. It benefits from the representation of the matrix as a sum of rank-one components which makes the algorithm attractive for online eigenvalue and eigenvector tracking applications. We examine the performance of the proposed algorithm in two types of important application examples: First, we consider the online eigendecomposition of a sample covariance matrix over the network, with application in decentralized Direction-of-Arrival (DoA) estimation and DoA tracking applications. 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. We apply our proposed algorithm to track the spectra of the graph Laplacian in static and dynamic networks. Simulation results reveal that the proposed algorithm outperforms existing decentralized algorithms both in terms of estimation accuracy as well as communication cost. |
| Freie Schlagworte: | Eigenvalues and eigenfunctions, Signal processing algorithms, Estimation, Heuristic algorithms, Partitioning algorithms, Covariance matrices, Direction-of-arrival estimation, Signal Processing (eess.SP), FOS: Electrical engineering, electronic engineering, information engineering, FOS: Electrical engineering, electronic engineering, information engineering |
| 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: | 18 Okt 2023 13:14 |
| Letzte Änderung: | 21 Mai 2024 07:18 |
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| Suche nach Titel in: | TUfind oder in Google |
Verfügbare Versionen dieses Eintrags
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Decentralized Eigendecomposition for Online Learning over Graphs with Applications. (deposited 06 Mär 2023 13:36)
- Decentralized Eigendecomposition for Online Learning over Graphs with Applications. (deposited 18 Okt 2023 13:14) [Gegenwärtig angezeigt]
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