Kernel matching pursuit for large datasets

POPOVICI, Vlad; S BENGIO a JP THIRAN. Kernel matching pursuit for large datasets. PATTERN RECOGNITION. OXFORD: PERGAMON-ELSEVIER SCIENCE LTD, 2005, roč. 38, č. 12, s. 2385-2390. ISSN 0031-3203. Dostupné z: https://doi.org/10.1016/j.patcog.2005.01.021.

Základní údaje

Originální název

Kernel matching pursuit for large datasets

Autoři

POPOVICI, Vlad; S BENGIO a JP THIRAN

Vydání

PATTERN RECOGNITION, OXFORD, PERGAMON-ELSEVIER SCIENCE LTD, 2005, 0031-3203

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Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Utajení

není předmětem státního či obchodního tajemství

Impakt faktor

Impact factor: 2.153

Označené pro přenos do RIV

Ne

DOI

https://doi.org/10.1016/j.patcog.2005.01.021

UT WoS

000232703000014

Klíčová slova anglicky

kernel matching pursuit; greedy algorithm; sparse classifier

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Anotace

V originále

Kernel matching pursuit is a greedy algorithm for building an approximation of a discriminant function as a linear combination of some basis functions selected from a kernel-induced dictionary. Here we propose a modification of the kernel matching pursuit algorithm that aims at making the method practical for large datasets. Starting from an approximating algorithm, the weak greedy algorithm, we introduce a stochastic method for reducing the search space at each iteration. Then we study the implications of using an approximate algorithm and we show how one can control the trade-off between the accuracy and the need for resources. Finally, we present some experiments performed on a large dataset that support our approach and illustrate its applicability. (c) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.