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Multi-output regression using a locally regularised orthogonal least square algorithm

Multi-output regression using a locally regularised orthogonal least square algorithm
Multi-output regression using a locally regularised orthogonal least square algorithm
The paper proposes a locally regularised orthogonal least squares (LROLS) algorithm for constructing sparse multi-output regression models that generalise well. By associating each regressor in the regression model with an individual regularisation parameter, the ability for the multi-output orthogonal least squares (OLS) model selection to produce a parsimonious model with good generalisation performance is greatly enhanced.
185-195
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53

Chen, S. (2002) Multi-output regression using a locally regularised orthogonal least square algorithm. IEE Proceedings - Vision, Image and Signal Processing, 149 (4), 185-195.

Record type: Article

Abstract

The paper proposes a locally regularised orthogonal least squares (LROLS) algorithm for constructing sparse multi-output regression models that generalise well. By associating each regressor in the regression model with an individual regularisation parameter, the ability for the multi-output orthogonal least squares (OLS) model selection to produce a parsimonious model with good generalisation performance is greatly enhanced.

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Published date: August 2002
Additional Information: submitted for publication in Oct. 2001, revised in Feb. 2002, accepted in March 2002
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 256833
URI: http://eprints.soton.ac.uk/id/eprint/256833
PURE UUID: 3f58e160-7bb7-4725-91f5-7f7c457c3b17

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Date deposited: 07 Oct 2002
Last modified: 04 Nov 2019 20:54

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Contributors

Author: S. Chen

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