Publication details
Kernel matching pursuit for large datasets
| Authors | |
|---|---|
| Year of publication | 2005 |
| Type | Article in Periodical |
| Magazine / Source | PATTERN RECOGNITION |
| Citation | |
| Doi | http://dx.doi.org/10.1016/j.patcog.2005.01.021 |
| Keywords | kernel matching pursuit; greedy algorithm; sparse classifier |
| Description | 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. |