Adaptive Message Passing Sign Algorithm
Abstract
A new algorithm named the Adaptive Message Passing Sign (AMPS) algorithm is introduced for online prediction, missing data imputation, and impulsive noise removal in time-varying graph signals. This work investigates the potential of message passing on spectral adaptive graph filters to define online localized node aggregations. AMPS updates a sign error derived from $l_1$-norm optimization between observation and estimation, leading to fast and robust predictions in the presence of impulsive noise. The combination of adaptive spectral graph filters with message passing reveals a different perspective on viewing message passing and vice versa. Testing on a real-world network formed by a map of nationwide weather stations, the AMPS algorithm accurately forecasts time-varying temperatures.
Cite
Text
Peng et al. "Adaptive Message Passing Sign Algorithm." NeurIPS 2023 Workshops: TGL, 2023.Markdown
[Peng et al. "Adaptive Message Passing Sign Algorithm." NeurIPS 2023 Workshops: TGL, 2023.](https://mlanthology.org/neuripsw/2023/peng2023neuripsw-adaptive/)BibTeX
@inproceedings{peng2023neuripsw-adaptive,
title = {{Adaptive Message Passing Sign Algorithm}},
author = {Peng, Changran and Yan, Yi and Kuruoglu, Ercan},
booktitle = {NeurIPS 2023 Workshops: TGL},
year = {2023},
url = {https://mlanthology.org/neuripsw/2023/peng2023neuripsw-adaptive/}
}