Paper Info
Title
Adaptive Learning in Cartesian Product of Reproducing Kernel Hilbert Spaces.
Abstract
We propose a novel adaptive learning algorithm based on iterative orthogonal projections in the Cartesian product of multiple reproducing kernel Hilbert spaces (RKHSs). The objective is to estimate or track nonlinear functions that are supposed to contain multiple components such as (i) linear and nonlinear components, (ii) high- and low- frequency components etc. In this case, the use of multiple RKHSs permits a compact representation of multicomponent functions. The proposed algorithm is where two different methods of the author meet: multikernel adaptive filtering and the algorithm of hyperplane projection along affine subspace (HYPASS). In a particular case, the ‘sum’ space of the RKHSs is isomorphic, under a straightforward correspondence, to the product space, and hence the proposed algorithm can also be regarded as an iterative projection method in the sum space. The efficacy of the proposed algorithm is shown by numerical examples.
Year
DOI
Venue
2014
10.1109/TSP.2015.2463261
Signal Processing, IEEE Transactions
Keywords
Field
DocType
Cartesian product,multikernel adaptive filtering,orthogonal projection,reproducing kernel Hilbert space
Kernel (linear algebra),Hilbert space,Mathematical optimization,Orthographic projection,Cartesian product,Kernel principal component analysis,Kernel adaptive filter,Hyperplane,Mathematics,Reproducing kernel Hilbert space
Journal
Volume
Issue
ISSN
abs/1408.0853
99
1053-587X
Citations 
PageRank 
References 
10
0.49
33
Authors
1
Name
Order
Citations
PageRank
Masahiro Yukawa127230.44