自适应图信号消息传递，实时处理噪声与缺失数据
Graph Signal Adaptive Message Passing
- 每个节点本地计算，动态调整消息传递策略
- 在高斯与脉冲噪声下实现精准预测与补全
- 适合实时图数据处理，如传感器网络、社交网络
本文提出图信号自适应消息传递（GSAMP），一种新型消息传递方法，可同时实现时间变化图信号的在线预测、缺失数据补全和噪声去除。与传统图信号处理方法对全图应用相同滤波器不同，GSAMP 的时空更新采用局部化计算，基于优化问题求解自适应方案，以最小化观测值与估计值之间的差异。该方法在高斯噪声和脉冲噪声条件下均能有效处理真实世界的时间变化图信号。
原文摘要 · Abstract (English)
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.
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