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arXiv Machine Learning By Haruki Yokota, Koki Yamada, Yuichi Tanaka, Antonio Ortega

Time-Varying Graph Learning with Constraints on Graph Temporal Variation

Read the original on arXiv Machine Learning →

The paper introduces a new framework for learning time‑varying graphs from spatiotemporal data, leveraging a prior on signal temporal behavior to estimate graphs from few measurements. It adds three convex regularization terms that enforce sparsity in the temporal changes of the network, and presents a scalable algorithm to solve the resulting optimization problem. Experiments on synthetic data and real datasets—including point clouds, temperature readings, and EEG signals—show that the method outperforms existing state‑of‑the‑art approaches.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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