Time-varying Graph Learning Based on Sparseness of Temporal Variation

Koki Yamada; Yuichi Tanaka; Antonio Ortega

ICASSP 2019accepted0 citations

Time-varying Graph Learning Based on Sparseness of Temporal Variation

Koki Yamada, Yuichi Tanaka, Antonio Ortega

Abstract

We propose a method for graph learning from spatiotemporal measurements. We aim at inferring time-varying graphs under the assumption that changes in graph topology and weights are sparse in time. The problem is formulated as a convex optimization problem to impose a constraint on the temporal relation of the time-varying graph. Experimental results with synthetic data show the effectiveness of our proposed method.

BibTeX

@inproceedings{icassp2019_timevaryinggraph,
  title = {Time-varying Graph Learning Based on Sparseness of Temporal Variation},
  author = {Koki Yamada and Yuichi Tanaka and Antonio Ortega},
  booktitle = {ICASSP 2019},
  year = {2019}
}