5 papers · 1 filter
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…
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…
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…
Cross-Spectral Analysis of Bivariate Graph Signals
Kyusoon Kim, Hee-Seok Oh
With the advancements in technology and monitoring tools, we often encounter multivariate graph signals, which can be seen as the realizations of multivariate graph processes, and…
Absolute average and median treatment effects as causal estimands on metric spaces
Ha-Young Shin, Kyusoon Kim, Kwonsang Lee +1
We define the notions of absolute average and median treatment effects as causal estimands on general metric spaces such as Riemannian manifolds, propose estimators using stratific…