2016/09/30 by Paolo Di Lorenzo, P. Di Lorenzo, P. Banelli +5 · 1 citation
Computer Science · Mathematics · #Advanced Graph Neural Networks #Artificial intelligence #Computer science #Face and Expression Recognition #Graph #Neural Networks and Applications #Radar #Signal processing #Telecommunications #Theoretical computer science #cs.LG #stat.ML
paper · pdf · doi:10.1109/tsp.2017.2708035
To appear in IEEE Transactions on Signal Processing, 2017
arxiv created 2017/05/13 · openalex publication_date 2017/05/25 · arxiv updated 2017/08/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.