EFFICIENT CLASSIFICATION FOR LARGE-SCALE PROBLEMS BY MULTIPLE LDA SUBSPACES

Martina Uray, Peter M. Roth, Horst Bischof

Abstract

In this paper we consider the limitations of Linear Discriminative Analysis (LDA) when applying it for largescale problems. Since LDA was originally developed for two-class problems the obtained transformation is sub-optimal if multiple classes are considered. In fact, the separability between the classes is reduced, which decreases the classification power. To overcome this problem several approaches including weighting strategies and mixture models were proposed. But these approaches are complex and computational expensive. Moreover, they were only tested for a small number of classes. In contrast, our approach allows to handle a huge number of classes showing excellent classification performance at low computational costs. The main idea is to split the original data into multiple sub-sets and to compute a single LDA space for each sub-set. Thus, the separability in the obtained subspaces is increased and the overall classification power is improved. Moreover, since smaller matrices have to be handled the computational complexity is reduced for both, training and classification. These benefits are demonstrated on different publicly available datasets. In particular, we consider the task of object recognition, where we can handle up to 1000 classes.

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Paper Citation


in Harvard Style

Uray M., M. Roth P. and Bischof H. (2009). EFFICIENT CLASSIFICATION FOR LARGE-SCALE PROBLEMS BY MULTIPLE LDA SUBSPACES . In Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009) ISBN 978-989-8111-69-2, pages 299-306. DOI: 10.5220/0001754802990306


in Bibtex Style

@conference{visapp09,
author={Martina Uray and Peter M. Roth and Horst Bischof},
title={EFFICIENT CLASSIFICATION FOR LARGE-SCALE PROBLEMS BY MULTIPLE LDA SUBSPACES},
booktitle={Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)},
year={2009},
pages={299-306},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001754802990306},
isbn={978-989-8111-69-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)
TI - EFFICIENT CLASSIFICATION FOR LARGE-SCALE PROBLEMS BY MULTIPLE LDA SUBSPACES
SN - 978-989-8111-69-2
AU - Uray M.
AU - M. Roth P.
AU - Bischof H.
PY - 2009
SP - 299
EP - 306
DO - 10.5220/0001754802990306