Paper Info
Title
On adaptivity of online model selection method based on multikernel adaptive filtering.
Abstract
We investigate adaptivity of the online model selection method which has been proposed recently within the multikernel adaptive filtering framework. Specifically, we consider a situation in which the nonlinear system under study changes during adaptation and an appropriate kernel also does accordingly. Our time-varying cost functions involve three regularizers: the l(1) norm and two block l(1) norms which promote sparsity both in the kernel and data groups. The block l(1) regularizers are approximated by their Moreau envelopes, and the adaptive proximal forward-backward splitting (APFBS) method is applied to the approximated cost function. Numerical examples show that the proposed algorithm can adaptively estimate a reasonable model.
Year
Venue
Keywords
2013
Asia-Pacific Signal and Information Processing Association Annual Summit and Conference
adaptive filters,approximation theory
Field
DocType
ISSN
Kernel (linear algebra),Mathematical optimization,Nonlinear system,Approximation theory,Multikernel,Adaptive filter,Kernel adaptive filter,Mathematics,Online model
Conference
2309-9402
Citations 
PageRank 
References 
4
0.47
7
Authors
2
Name
Order
Citations
PageRank
Masahiro Yukawa127230.44
Ryu-ichiro Ishii240.47