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Locally regularised orthogonal least squares algorithm for the construction of sparse kernel regression models

Locally regularised orthogonal least squares algorithm for the construction of sparse kernel regression models
Locally regularised orthogonal least squares algorithm for the construction of sparse kernel regression models
The paper proposes to combine an orthogonal least squares (OLS) subset 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. Two examples are used to illustrate the proposed algorithm.
1229-1232
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53

Chen, S. (2002) Locally regularised orthogonal least squares algorithm for the construction of sparse kernel regression models. 6th Int. Cof. Signal Processing, China. 26 - 30 Aug 2002. pp. 1229-1232 .

Record type: Conference or Workshop Item (Other)

Abstract

The paper proposes to combine an orthogonal least squares (OLS) subset 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. Two examples are used to illustrate the proposed algorithm.

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More information

Published date: August 2002
Additional Information: presented at 6th International Conference on Signal Processing (Beijing, China), August 26-30, 2002 Event Dates: August 26-30, 2002 Organisation: IEEE SP Society, IEE, Chinese Institute of Electronics
Venue - Dates: 6th Int. Cof. Signal Processing, China, 2002-08-26 - 2002-08-30
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 256811
URI: http://eprints.soton.ac.uk/id/eprint/256811
PURE UUID: 5d3565f5-96e4-45c6-b8bc-92bbb2d14df1

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Date deposited: 07 Oct 2002
Last modified: 16 Dec 2019 20:46

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Contributors

Author: S. Chen

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