Sparse Learning of Kernel Transfer Operators

Boya Hou, Subhonmesh Bose, Umesh Vaidya

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Transfer operators such as the Koopman and the Perron-Frobenius operators provide valuable insights into the properties of nonlinear dynamical systems. Recent work has shown that non-parametric approximations of these operators can be constructed over reproducing kernel Hilbert space (RKHS) with data. These kernel transfer operators can then be written as functions of covariance and cross-covariance operators associated with the data generated by the dynamical system. In this paper, we study sparse kernel learning methods for kernel transfer operators. Specifically, we study sample complexity guarantees for coherency-based sparsification and demonstrate its efficacy over an example dynamical system.

Original languageEnglish (US)
Title of host publication55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781665458283
StatePublished - 2021
Externally publishedYes
Event55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021 - Virtual, Pacific Grove, United States
Duration: Oct 31 2021Nov 3 2021

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393


Conference55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021
Country/TerritoryUnited States
CityVirtual, Pacific Grove

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications


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