eprintid: 3759
rev_number: 7
eprint_status: archive
userid: 69
dir: disk0/00/00/37/59
datestamp: 2017-08-04 11:47:46
lastmod: 2017-08-04 11:47:46
status_changed: 2017-08-04 11:47:46
type: article
metadata_visibility: show
creators_name: Gnecco, Giorgio
creators_name: Bemporad, Alberto
creators_name: Gori, Marco
creators_name: Sanguineti, Marcello
creators_id: giorgio.gnecco@imtlucca.it
creators_id: alberto.bemporad@imtlucca.it
creators_id: 
creators_id: 
title: LQG Online Learning
ispublished: pub
subjects: QA75
divisions: CSA
full_text_status: public
abstract: Optimal control theory and machine learning techniques are combined to formulate and solve in closed form an optimal control formulation of online learning from supervised examples with regularization of the updates. The connections with the classical linear quadratic gaussian (LQG) optimal control problem, of which the proposed learning paradigm is a nontrivial variation as it involves random matrices, are investigated. The obtained optimal solutions are compared with the Kalman filter estimate of the parameter vector to be learned. It is shown that the proposed algorithm is less sensitive to outliers with respect to the Kalman estimate (thanks to the presence of the regularization term), thus providing smoother estimates with respect to time. The basic formulation of the proposed online learning framework refers to a discrete-time setting with a finite learning horizon and a linear model. Various extensions are investigated, including the infinite learning horizon and, via the so-called kernel trick, the case of nonlinear models.
date: 2017
publication: Neural Computation
volume: 29
number: 8
publisher: MIT Press
pagerange: 2203-2291
id_number: doi:10.1162/neco_a_00976
refereed: TRUE
issn: 0899-7667
official_url: http://doi.org/10.1162/neco_a_00976
citation:   Gnecco, Giorgio and Bemporad, Alberto and Gori, Marco and Sanguineti, Marcello  LQG Online Learning.  Neural Computation, 29 (8).  pp. 2203-2291.  ISSN 0899-7667      (2017)  
document_url: http://eprints.imtlucca.it/3759/1/1606.04272.pdf