| Paper ID | B-1-2.1 |
| Paper Title |
LEARNING GRAPHS WITH MULTIPLE TEMPORAL RESOLUTIONS |
| Authors |
Koki Yamada, Yuichi Tanaka, Tokyo University of Agriculture and Technology, Japan |
| Session |
B-1-2: Adaptive and Intelligent Signal Processing |
| Time | Tuesday, 08 December, 15:30 - 17:00 |
| Presentation Time: | Tuesday, 08 December, 15:30 - 15:45 Check your Time Zone |
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All times are in New Zealand Time (UTC +13) |
| Topic |
Signal and Information Processing Theory and Methods (SIPTM): |
| Abstract |
This paper proposes a framework for learning time-varying graphs with multiple temporal resolutions from multivariate time series signals. Our method estimates multiresolution graphs by a top-down approach: Graphs are learned from a segment of the time-series data corresponding to the desired temporal resolution, and we impose a constraint so that the learned graphs at the target temporal resolution are close to that in the lower temporal resolution. The proposed approach overcomes the problem of existing time-varying graph learning methods that must infer graphs in a single temporal resolution. Experimental results with synthetic data demonstrate that our method outperforms a baseline graph learning method. |