Domain Adaptation in Physical Systems via Graph Kernel

Haoran Li, Hanghang Tong, Yang Weng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationKDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages868-876
Number of pages9
ISBN (Electronic)9781450393850
DOIs
StatePublished - Aug 14 2022
Event28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022 - Washington, United States
Duration: Aug 14 2022Aug 18 2022

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022
Country/TerritoryUnited States
CityWashington
Period8/14/228/18/22

Keywords

  • domain adaptation
  • graph coarsening
  • graph kernels
  • networked data
  • physical system

ASJC Scopus subject areas

  • Software
  • Information Systems

Fingerprint

Dive into the research topics of 'Domain Adaptation in Physical Systems via Graph Kernel'. Together they form a unique fingerprint.

Cite this