DocumentCode :
3180667
Title :
Locally regularised orthogonal least squares algorithm for the construction of sparse kernel regression models
Author :
Chen, Sheng
Author_Institution :
Dept. of Electron. & Comput. Sci., Southampton Univ., UK
Volume :
2
fYear :
2002
fDate :
26-30 Aug. 2002
Firstpage :
1229
Abstract :
The paper proposes to combine orthogonal least squares (OLS) model selection with local regularisation for efficient sparse kernel data modelling. By assigning each orthogonal weight in the regression model with an individual regularisation parameter, the ability for the OLS model selection to produce a very parsimonious model with excellent generalisation performance is greatly enhanced.
Keywords :
Hessian matrices; data models; least squares approximations; statistical analysis; Hessian matrix; data modelling; learning procedure; local regularisation; orthogonal least squares algorithm; sparse kernel regression models; Bayesian methods; Computer science; Diversity reception; Iterative algorithms; Kernel; Learning systems; Least squares methods; Matrix decomposition; Optimization methods; Signal to noise ratio;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing, 2002 6th International Conference on
Print_ISBN :
0-7803-7488-6
Type :
conf
DOI :
10.1109/ICOSP.2002.1180013
Filename :
1180013
Link To Document :
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