Paper Info
Title
Multi-Domain Adaptive Learning Based On Feasibility Splitting And Adaptive Projected Subgradient Method
Abstract
We propose the multi-domain adaptive learning that enables us to find a point meeting possibly time-varying specifications simultaneously in multiple domains. e.g. space, time, frequency, etc. The novel concept is based on the idea of feasibility splitting - dealing with feasibility in each individual domain. We show that the adaptive projected subgradient method (Yamada, 2003) realizes the multi-domain adaptive learning by employing (i) a projected gradient operator with respect to a 'fixed' proximity function reflecting the time-invariant specifications and (ii) a subgradient projection with respect to 'time-varying' objective functions reflecting the time-varying specifications. The resulting algorithm is Suitable for real-time implementation, because it requires no more than metric projections onto closed convex sets each of which accommodates the specification in each domain. A convergence analysis and numerical examples are presented.
Year
DOI
Venue
2010
10.1587/transfun.E93.A.456
IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES
Keywords
Field
DocType
adaptive algorithm, convex projection, projected gradient method, convex feasibility problem
Convergence (routing),Mathematical optimization,Subgradient method,Adaptive projected subgradient method,Regular polygon,Multi domain,Operator (computer programming),Adaptive algorithm,Adaptive learning,Mathematics
Journal
Volume
Issue
ISSN
E93A
2
0916-8508
Citations 
PageRank 
References 
10
0.77
13
Authors
3
Name
Order
Citations
PageRank
Masahiro Yukawa127230.44
Konstantinos Slavakis258340.76
isao yamada395374.52