Efficient formation of a basis in a kernel induced feature space

Cawley, Gavin C. ORCID: https://orcid.org/0000-0002-4118-9095 and Talbot, Nicola L. C. (2002) Efficient formation of a basis in a kernel induced feature space. In: European Symposium on Artificial Neural Networks (ESANN 2002), 2002-04-24 - 2002-04-26.

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Abstract

Baudat and Anouar [1] propose a simple greedy algorithm for estimation of an approximate basis of the subspace spanned by a set of fixed vectors embedded in a kernel induced feature space. The resulting set of basis vectors can then be used to construct sparse kernel expansions for classification and regression tasks. In this paper we describe five algorithmic improvements to the method of Baudat and Anouar, allowing the construction of an approximate basis with a computational complexity that is independent of the number of training patterns, depending only on the number of basis vectors extracted.

Item Type:	Conference or Workshop Item (Paper)
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
Depositing User:	Vishal Gautam
Date Deposited:	27 Jul 2011 12:27
Last Modified:	15 Dec 2022 01:05
URI:	https://ueaeprints.uea.ac.uk/id/eprint/22160
DOI:

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