Low-rank Updates in Slowly Time-varying Graphs for Spatial-Temporal Signal Interpolation
A crucial assumption in graph signal processing (GSP) is the existence of an underlying graph that captures the pairwise similarities between nodes, allowing filters to be designed based on this graph for tasks such as denoising. For spatial-temporal data in which node-to-node similarities evolve over time, a static spatial graph is insufficient. In this paper, to represent slowly time-varying pairwise relationships, we model the graph changes in two consecutive adjacency matrices $P = W^{(2)} - W^{(1)}$ across time as a low-rank matrix. % Specifically, given an initial adjacency matrix $W^{(1)}$ at time $t=1$, we jointly interpolate a signal $x_2$ and estimate $W^{(2)}$ at $t=2$ using bo...
| Authors | Saghar Bagheri, Gene Cheung, Tim Eadie et al. |
|---|---|
| Primary category | eess.SP |
| Published | 22 June 2026 |
| Last updated at source | 22 June 2026 |
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