PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Kernel Matching Pursuit for Large Datasets
Vlad Popovici, Samy Bengio and Jean-Philippe Thiran
Pattern Recognition Volume 38, Number 12, pp. 2385-2390, 2005.

Abstract

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 aim s 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.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:1294
Deposited By:Samy Bengio
Deposited On:28 November 2005