Efficient formation of a basis in a kernel induced feature space

Gavin C. Cawley; Nicola L. C. Talbot

Efficient formation of a basis in a kernel induced feature space

Research output: Contribution to conference › Paper

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.

Original language	English
Pages	1-6
Number of pages	6
Publication status	Published - Apr 2002
Event	European Symposium on Artificial Neural Networks (ESANN 2002) - Bruges, Belgium Duration: 24 Apr 2002 → 26 Apr 2002

Conference

Conference	European Symposium on Artificial Neural Networks (ESANN 2002)
Country/Territory	Belgium
City	Bruges
Period	24/04/02 → 26/04/02

Cite this

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title = "Efficient formation of a basis in a kernel induced feature space",

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.",

author = "Cawley, {Gavin C.} and Talbot, {Nicola L. C.}",

year = "2002",

month = apr,

language = "English",

pages = "1--6",

note = "European Symposium on Artificial Neural Networks (ESANN 2002) ; Conference date: 24-04-2002 Through 26-04-2002",

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AU - Cawley, Gavin C.

AU - Talbot, Nicola L. C.

PY - 2002/4

Y1 - 2002/4

N2 - 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.

AB - 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.

M3 - Paper

SP - 1

EP - 6

T2 - European Symposium on Artificial Neural Networks (ESANN 2002)

Y2 - 24 April 2002 through 26 April 2002

ER -