Graph Signal Adaptive Message Passing
This addresses a domain-specific problem for graph signal processing, offering an incremental improvement over conventional methods.
The paper tackles the problem of processing time-varying graph signals under noise by proposing Graph Signal Adaptive Message Passing (GSAMP), which simultaneously performs online prediction, missing data imputation, and noise removal, showing effectiveness under Gaussian and impulsive noise conditions.
This paper proposes Graph Signal Adaptive Message Passing (GSAMP), a novel message passing method that simultaneously conducts online prediction, missing data imputation, and noise removal on time-varying graph signals. Unlike conventional Graph Signal Processing methods that apply the same filter to the entire graph, the spatiotemporal updates of GSAMP employ a distinct approach that utilizes localized computations at each node. This update is based on an adaptive solution obtained from an optimization problem designed to minimize the discrepancy between observed and estimated values. GSAMP effectively processes real-world, time-varying graph signals under Gaussian and impulsive noise conditions.