Paper Info
Title
An efficient data-reusing kernel adaptive filtering algorithm based on Parallel HYperslab Projection along Affine Subspaces
Abstract
We propose a novel kernel adaptive filtering algorithm, dubbed Parallel HYperslab Projection along Affine Sub-Spaces (Φ-PASS), which reuses observed data efficiently. We first derive its fully-updating version that projects the current filter onto multiple hyperslabs in parallel along the dictionary subspace. Each hyperslab accommodates one of the data observed up to the present time instant. The algorithm is derived with the adaptive projected subgradient method (APSM) based on which a convergence analysis is presented. We then generalize the algorithm so that only a few coefficients, whose associated dictionary-data are coherent to the datum of each hyperslab, can be updated selectively for low complexity. This is accomplished by performing the hyperslab projections along affine subspaces defined with the selected dictionary-data. Numerical examples show the efficacy of the proposed algorithm.
Year
DOI
Venue
2013
10.1109/ICASSP.2013.6638320
Acoustics, Speech and Signal Processing
Keywords
Field
DocType
adaptive filters,affine transforms,convergence of numerical methods,dictionaries,gradient methods,Φ-PASS,APSM,adaptive projected subgradient method,convergence analysis,dictionary data subspace,efficient data-reusing kernel adaptive filtering algorithm,parallel hyperslab projection along affine subspace,kernel adaptive filter,projection algorithms,reproducing kernel Hilbert space,the HYPASS algorithm
Affine transformation,Kernel (linear algebra),Mathematical optimization,Algorithm design,Subspace topology,Computer science,Adaptive filter,Kernel adaptive filter,Reproducing kernel Hilbert space,Computational complexity theory
Conference
ISSN
Citations 
PageRank 
1520-6149
5
0.49
References 
Authors
8
2
Name
Order
Citations
PageRank
Masa-aki Takizawa1131.96
Masahiro Yukawa227230.44