Distributed Adaptive Learning of Graph Signals
arXiv:1609.06100 · doi:10.1109/TSP.2017.2708035
Abstract
The aim of this paper is to propose distributed strategies for adaptive learning of signals defined over graphs. Assuming the graph signal to be bandlimited, the method enables distributed reconstruction, with guaranteed performance in terms of mean-square error, and tracking from a limited number of sampled observations taken from a subset of vertices. A detailed mean square analysis is carried out and illustrates the role played by the sampling strategy on the performance of the proposed method. Finally, some useful strategies for distributed selection of the sampling set are provided. Several numerical results validate our theoretical findings, and illustrate the performance of the proposed method for distributed adaptive learning of signals defined over graphs.
To appear in IEEE Transactions on Signal Processing, 2017
References in corpus (7)
- Discrete Signal Processing on Graphs
- Signals on Graphs: Uncertainty Principle and Sampling
- Sampling of graph signals with successive local aggregations
- Local-set-based Graph Signal Reconstruction
- Adaptive Least Mean Squares Estimation of Graph Signals
- Reconstruction of Graph Signals through Percolation from Seeding Nodes
- Multitask diffusion adaptation over networks with common latent representations
Cited by in corpus (6)
- Adaptive Graph Signal Processing: Algorithms and Optimal Sampling Strategies
- Distributed Training of Graph Convolutional Networks
- Observing and Tracking Bandlimited Graph Processes
- Graph Normalized-LMP Algorithm for Signal Estimation Under Impulsive Noise
- Sampling and Inference of Networked Dynamics using Log-Koopman Nonlinear Graph Fourier Transform
- Online Distributed Learning over Graphs with Multitask Graph-Filter Models