Sparse multiple kernels for impulse response estimation with majorization minimization algorithms
2012 (English)In: Decision and Control (CDC), 2012, IEEE , 2012, 1500-1505 p.Conference paper (Refereed)
This contribution aims to enrich the recently introduced kernel-based regularization method for linear system identification. Instead of a single kernel, we use multiple kernels, which can be instances of any existing kernels for the impulse response estimation of linear systems. We also introduce a new class of kernels constructed based on output error (OE) model estimates. In this way, a more flexible and richer representation of the kernel is obtained. Due to this representation the associated hyper-parameter estimation problem has two good features. First, it is a difference of convex functions programming (DCP) problem. While it is still nonconvex, it can be transformed into a sequence of convex optimization problems with majorization minimization (MM) algorithms and a local minima can thus be found iteratively. Second, it leads to sparse hyper-parameters and thus sparse multiple kernels. This feature shows the kernel-based regularization method with multiple kernels has the potential to tackle various problems of finding sparse solutions in linear system identification.
Place, publisher, year, edition, pages
IEEE , 2012. 1500-1505 p.
Engineering and Technology
IdentifiersURN: urn:nbn:se:liu:diva-103267DOI: 10.1109/CDC.2012.6426801ISI: 000327200401142ISBN: 978-1-4673-2065-8 (print)ISBN: 978-1-4673-2064-1 (online)OAI: oai:DiVA.org:liu-103267DiVA: diva2:688527
2012 IEEE 51st Annual Conference on Decision and Control (CDC), 10-13 December 2012, Maui, HI, USA