NFFT Meets Krylov Methods: Fast Matrix-Vector Products for the Graph Laplacian of Fully Connected Networks

NFFT Meets Krylov Methods: Fast Matrix-Vector Products for the Graph Laplacian of Fully Connected Networks

The graph Laplacian is a standard tool in data science, machine learning, and image processing. The corresponding matrix inherits the complex structure of the underlying network and is in certain applications densely populated. This makes computations, in particular matrix-vector products, with the...

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Bibliographic Details
Main Authors: Dominik Alfke, Daniel Potts, Martin Stoll, Toni Volkmer
Format: Article
Language:English
Published: Frontiers Media S.A. 2018-12-01
Series:Frontiers in Applied Mathematics and Statistics
Subjects:
Graph Laplacian
Lanczos method
eigenvalues
nonequispaced fast Fourier transform
machine learning
Online Access:https://www.frontiersin.org/article/10.3389/fams.2018.00061/full

Internet

https://www.frontiersin.org/article/10.3389/fams.2018.00061/full

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