Time-Varying Graph Learning with Constraints on Graph Temporal Variation

• URL: http://arxiv.org/abs/2001.03346v1
• Date: Fri, 10 Jan 2020 08:33:51 GMT
• Title: Time-Varying Graph Learning with Constraints on Graph Temporal Variation
• Authors: Koki Yamada, Yuichi Tanaka, Antonio Ortega
• Abstract summary: We introduce two regularization terms in convex optimization problems that constrain sparseness of temporal variations of time-varying networks. A computationally-scalable algorithm is introduced to efficiently solve the optimization problem.
• Score: 46.218671952531324
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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