Time-Varying Graph Learning with Constraints on Graph Temporal Variation
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2020
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| Subjects: | |
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| _version_ | 1866915484528541696 |
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| author | Yokota, Haruki Yamada, Koki Tanaka, Yuichi Ortega, Antonio |
| author_facet | Yokota, Haruki Yamada, Koki Tanaka, Yuichi Ortega, Antonio |
| contents | We propose a novel framework for learning time-varying graphs from spatiotemporal measurements. Given an appropriate prior on the temporal behavior of signals, our proposed method can estimate time-varying graphs from a small number of available measurements. To achieve this, we introduce two regularization terms in convex optimization problems that constrain sparseness of temporal variations of the time-varying networks. Moreover, a computationally-scalable algorithm is introduced to efficiently solve the optimization problem. The experimental results with synthetic and real datasets (point cloud and temperature data) demonstrate our proposed method outperforms the existing state-of-the-art methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2001_03346 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Time-Varying Graph Learning with Constraints on Graph Temporal Variation Yokota, Haruki Yamada, Koki Tanaka, Yuichi Ortega, Antonio Signal Processing Machine Learning We propose a novel framework for learning time-varying graphs from spatiotemporal measurements. Given an appropriate prior on the temporal behavior of signals, our proposed method can estimate time-varying graphs from a small number of available measurements. To achieve this, we introduce two regularization terms in convex optimization problems that constrain sparseness of temporal variations of the time-varying networks. Moreover, a computationally-scalable algorithm is introduced to efficiently solve the optimization problem. The experimental results with synthetic and real datasets (point cloud and temperature data) demonstrate our proposed method outperforms the existing state-of-the-art methods. |
| title | Time-Varying Graph Learning with Constraints on Graph Temporal Variation |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2001.03346 |