研究噪声图信号重构与采样对分类任务的影响，提出新采样方法提升性能。
On the Impact of Downstream Tasks on Sampling and Reconstructing Noisy Graph Signals
- 基于分类误差构建通用理论框架，适用于多种重构方法。
- 在线性化GCN上推导出新最优采样策略，分类准确率显著提升。
- 适合图神经网络、信号处理领域研究人员参考。
我们研究了用于分类任务的图信号重构与采样选择问题。提出了适用于多种常用重构方法的分类误差通用理论表征，并与经典重构误差进行对比。通过该理论推导出线性化图卷积网络的新最优采样方法，实验表明其在多个数据集上优于传统基于图信号处理的方法，验证了理论的有效性。
原文摘要 · Abstract (English)
We investigate graph signal reconstruction and sample selection for classification tasks. We present general theoretical characterisations of classification error applicable to multiple commonly used reconstruction methods, and compare that to the classical reconstruction error. We demonstrate the applicability of our results by using them to derive new optimal sampling methods for linearized graph convolutional networks, and show improvement over other graph signal processing based methods.
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