This paper presents a nonparametric Bayesian interpretation of kernel-based function learning with manifold regularization. We show that manifold regularization corresponds to an additional likelihood term derived from noisy observations of the function gradient along the regressors graph. The hyperparameters of the method are estimated by a suitable empirical Bayes approach. The effectiveness of the method in the context of dynamical system identification is evaluated on a simulated linear system and on an experimental switching system setup.

(2022). Kernel-based system identification with manifold regularization: A Bayesian perspective [journal article - articolo]. In AUTOMATICA. Retrieved from http://hdl.handle.net/10446/220011

Kernel-based system identification with manifold regularization: A Bayesian perspective

Mazzoleni, Mirko;Scandella, Matteo;Previdi, Fabio
2022-01-01

Abstract

This paper presents a nonparametric Bayesian interpretation of kernel-based function learning with manifold regularization. We show that manifold regularization corresponds to an additional likelihood term derived from noisy observations of the function gradient along the regressors graph. The hyperparameters of the method are estimated by a suitable empirical Bayes approach. The effectiveness of the method in the context of dynamical system identification is evaluated on a simulated linear system and on an experimental switching system setup.
articolo
2022
Mazzoleni, Mirko; Chiuso, Alessandro; Scandella, Matteo; Formentin, Simone; Previdi, Fabio
(2022). Kernel-based system identification with manifold regularization: A Bayesian perspective [journal article - articolo]. In AUTOMATICA. Retrieved from http://hdl.handle.net/10446/220011
File allegato/i alla scheda:
File Dimensione del file Formato  
1-s2.0-S0005109822002722-main.pdf

Solo gestori di archivio

Versione: publisher's version - versione editoriale
Licenza: Licenza default Aisberg
Dimensione del file 1.02 MB
Formato Adobe PDF
1.02 MB Adobe PDF   Visualizza/Apri
Pubblicazioni consigliate

Aisberg ©2008 Servizi bibliotecari, Università degli studi di Bergamo | Terms of use/Condizioni di utilizzo

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/220011
Citazioni
  • Scopus 6
  • ???jsp.display-item.citation.isi??? 5
social impact