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Cawley, G. C. 
ORCID: https://orcid.org/0000-0002-4118-9095 and Talbot, N. L. C.
(2002)
Reduced rank kernel ridge regression.
Neural Processing Letters, 16 (3).
pp. 293-302.
ISSN 1370-4621
Abstract
Ridge regression is a classical statistical technique that attempts to address the bias-variance trade-off in the design of linear regression models. A reformulation of ridge regression in dual variables permits a non-linear form of ridge regression via the well-known 'kernel trick'. Unfortunately, unlike support vector regression models, the resulting kernel expansion is typically fully dense. In this paper, we introduce a reduced rank kernel ridge regression (RRKRR) algorithm, capable of generating an optimally sparse kernel expansion that is functionally identical to that resulting from conventional kernel ridge regression (KRR). The proposed method is demonstrated to out-perform an alternative sparse kernel ridge regression algorithm on the Motorcycle and Boston Housing benchmarks.
| Item Type: | Article |
|---|---|
| Faculty \ School: | Faculty of Science > School of Computing Sciences University of East Anglia > Faculty of Science > Research Groups > Computational Biology (subgroups are shown below) > Machine learning in computational biology |
| Related URLs: | |
| Depositing User: | Vishal Gautam |
| Date Deposited: | 13 Jun 2011 13:01 |
| Last Modified: | 24 Oct 2022 03:14 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/22159 |
| DOI: | 10.1023/A:1021798002258 |
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