Sparse data modelling using combined locally regularized orthogonal least squares and D-optimality design
Sparse data modelling using combined locally regularized orthogonal least squares and D-optimality design
The paper proposes to combine a locally regularized orthogonal least squares (LROLS) model selection with a D-optimality experimental design for efficient and robust sparse kernel data modelling. The LROLS algorithm alone is capable of producing a very parsimonious model with excellent generalization performance. The D-optimality design criterion further enhances the model efficiency and robustness. An added advantage is that the user only needs to specify a weighting for the D-optimality cost in the combined model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.
0 95338904 9
112-117
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
Hong, X.
b8f251c3-e142-4555-a54c-c504de966b03
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
September 2002
Chen, S.
9310a111-f79a-48b8-98c7-383ca93cbb80
Hong, X.
b8f251c3-e142-4555-a54c-c504de966b03
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Chen, S., Hong, X. and Harris, C.J.
(2002)
Sparse data modelling using combined locally regularized orthogonal least squares and D-optimality design.
Combined Annual Conf. Institute of Automation, the Chinese Academy of Sciences, and Annual Conf. Chinese Automation and Computer Society in U.K., Beijing, China.
20 - 21 Sep 2002.
.
Record type:
Conference or Workshop Item
(Other)
Abstract
The paper proposes to combine a locally regularized orthogonal least squares (LROLS) model selection with a D-optimality experimental design for efficient and robust sparse kernel data modelling. The LROLS algorithm alone is capable of producing a very parsimonious model with excellent generalization performance. The D-optimality design criterion further enhances the model efficiency and robustness. An added advantage is that the user only needs to specify a weighting for the D-optimality cost in the combined model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.
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Published date: September 2002
Additional Information:
Presented at Combined Annual Conf. Institute of Automation, the Chinese Academy of Sciences, and Annual Conf. Chinese Automation and Computer Society in U.K.} (Beijing, China), Sept.20-21, 2002 Event Dates: Sept.20-21, 2002 Organisation: Chinese Automation and Computer Science Society in U.K. and the Institute of Automation of the Chinese Academy of Sciences
Venue - Dates:
Combined Annual Conf. Institute of Automation, the Chinese Academy of Sciences, and Annual Conf. Chinese Automation and Computer Society in U.K., Beijing, China, 2002-09-20 - 2002-09-21
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 256821
URI: http://eprints.soton.ac.uk/id/eprint/256821
ISBN: 0 95338904 9
PURE UUID: c7f1d79c-b1b9-451b-8dc6-2caf4be36d03
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Date deposited: 07 Oct 2002
Last modified: 14 Mar 2024 05:48
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Contributors
Author:
S. Chen
Author:
X. Hong
Author:
C.J. Harris
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