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arXiv Machine Learning By Joel-Pascal Ntwali N'konzi, Feliks N\"{u}ske, Stefan Klus

Bilevel optimization for data-driven learning of Koopman embeddings using kernel-based autoencoders

Read the original on arXiv Machine Learning →

The paper introduces EDMD-kDL, a kernel-based method that learns finite-dimensional Koopman embeddings directly from data using bilevel optimization and collocation techniques. It combines kernel dictionary learning with extended dynamic mode decomposition to produce Koopman approximations without pre-specifying a dictionary. Experiments on sea-surface-temperature forecasting and video data show that EDMD‑kDL matches or surpasses ANN-based approaches while remaining scalable to large datasets.

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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