3 papers
stat.ME2026
Bayesian Node-Level Outlier Detection for Graph Signals
Seongmin Kim, Kyusoon Kim
This paper proposes a fully Bayesian framework for node-level outlier detection in graph signals, where measurements are observed on the nodes of an underlying graph. Unlike tradit…
stat.ME2026
Graph Canonical Coherence Analysis
Kyusoon Kim, Hee-Seok Oh
We propose graph canonical coherence analysis (gCChA), a novel framework that extends canonical correlation analysis to multivariate graph signals in the graph frequency domain. Th…
stat.ME2024
Principal Component Analysis in the Graph Frequency Domain
Kyusoon Kim, Hee-Seok Oh
We propose a novel principal component analysis in the graph frequency domain for dimension reduction of multivariate data residing on graphs. The proposed method not only effectiv…