TU Darmstadt / ULB / TUbiblio

Decentralized Eigendecomposition for Online Learning over Graphs

Fan, Yufan ; Trinh-Hoang, Minh ; Pesavento, Marius (2021)
Decentralized Eigendecomposition for Online Learning over Graphs.
29th European Signal Processing Conference (EUSIPCO). Dublin, Ireland (23.-27.08.2021)
doi: 10.23919/EUSIPCO54536.2021.9616034
Conference or Workshop Item, Bibliographie

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.

Item Type: Conference or Workshop Item
Erschienen: 2021
Creators: Fan, Yufan ; Trinh-Hoang, Minh ; Pesavento, Marius
Type of entry: Bibliographie
Title: Decentralized Eigendecomposition for Online Learning over Graphs
Language: English
Date: 8 December 2021
Publisher: IEEE
Book Title: 29th European Signal Processing Conference (EUSIPCO 2021): Proceedings
Event Title: 29th European Signal Processing Conference (EUSIPCO)
Event Location: Dublin, Ireland
Event Dates: 23.-27.08.2021
DOI: 10.23919/EUSIPCO54536.2021.9616034
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.

Divisions: 18 Department of Electrical Engineering and Information Technology
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Communication Systems
Date Deposited: 06 Mar 2023 11:15
Last Modified: 12 Jul 2023 15:04
PPN: 509537847
Export:
Suche nach Titel in: TUfind oder in Google
Send an inquiry Send an inquiry

Options (only for editors)
Show editorial Details Show editorial Details