Generalized Kernel-Based Dynamic Mode Decomposition
Machine Learning
2020-02-23 v1 Optimization and Control
Machine Learning
Abstract
Reduced modeling in high-dimensional reproducing kernel Hilbert spaces offers the opportunity to approximate efficiently non-linear dynamics. In this work, we devise an algorithm based on low rank constraint optimization and kernel-based computation that generalizes a recent approach called "kernel-based dynamic mode decomposition". This new algorithm is characterized by a gain in approximation accuracy, as evidenced by numerical simulations, and in computational complexity.
Cite
@article{arxiv.2002.04375,
title = {Generalized Kernel-Based Dynamic Mode Decomposition},
author = {Patrick Heas and Cedric Herzet and Benoit Combes},
journal= {arXiv preprint arXiv:2002.04375},
year = {2020}
}
Comments
45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020). arXiv admin note: substantial text overlap with arXiv:1710.10919