Domain Adaptation in Physical Systems via Graph Kernel
Domain Adaptation in Physical Systems via Graph Kernel
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DOI：
10.1145/3534678.3539380
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发表时间：
2022-08
期刊：
影响因子：
--
通讯作者：
Haoran Li;H. Tong;Yang Weng
中科院分区：
文献类型：
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作者：
Haoran Li;H. Tong;Yang Weng
物理系统正在通过低成本、低功耗的传感器以及先进的数据挖掘和机器学习技术将其监测能力扩展到边缘区域。然而，新系统用于训练模型的数据往往有限，需要从其他相关网格进行有效的知识转移。具体而言，域自适应（DA）寻求域不变特征，以提高目标域中的模型性能。尽管如此，现有的DA技术面临着巨大的挑战，由于物理数据集的独特特征：（1）复杂的时空相关性，（2）不同的数据源，包括节点/边缘测量和标签，以及（3）大规模的数据大小。本文提出了一种基于图核和图粗化两个核心设计的新型交叉图DA。前者的设计处理时空相关性，可以方便地将网络化的测量和标签。空间结构、时间趋势、度量相似性和标签信息共同决定了两个图的相似性，指导DA找到域不变特征。在数学上，我们构建了一个基于图内核的分布自适应（GNA）与一个专门设计的图内核。然后证明了所提出的核是正定的和普适的，从而严格保证了所用DA测度的可行性。然而，内核的计算成本对于大型系统来说是过高的。作为回应，我们提出了一种新的粗化过程，以获得更小的图形GNA。最后，我们报告GNA在各种系统中的优势，包括电力系统，质量阻尼器系统，和人类活动传感系统。
Physical systems are extending their monitoring capacities to edge areas with low-cost, low-power sensors and advanced data mining and machine learning techniques. However, new systems often have limited data for training the model, calling for effective knowledge transfer from other relevant grids. Specifically, Domain Adaptation (DA) seeks domain-invariant features to boost the model performance in the target domain. Nonetheless, existing DA techniques face significant challenges due to the unique characteristics of physical datasets: (1) complex spatial-temporal correlations, (2) diverse data sources including node/edge measurements and labels, and (3) large-scale data sizes. In this paper, we propose a novel cross-graph DA based on two core designs of graph kernels and graph coarsening. The former design handles spatial-temporal correlations and can incorporate networked measurements and labels conveniently. The spatial structures, temporal trends, measurement similarity, and label information together determine the similarity of two graphs, guiding the DA to find domain-invariant features. Mathematically, we construct a Graph kerNel-based distribution Adaptation (GNA) with a specifically-designed graph kernel. Then, we prove the proposed kernel is positive definite and universal, which strictly guarantees the feasibility of the used DA measure. However, the computation cost of the kernel is prohibitive for large systems. In response, we propose a novel coarsening process to obtain much smaller graphs for GNA. Finally, we report the superiority of GNA in diversified systems, including power systems, mass-damper systems, and human-activity sensing systems.