Linear Discriminant Analysis based on Fast Approximate SVD

Nassara Elhadji Ille Gado, Edith Grall-Maës, Malika Kharouf

2017

Abstract

We present an approach for performing linear discriminant analysis (LDA) in the contemporary challenging context of high dimensionality. The projection matrix of LDA is usually obtained by simultaneously maximizing the between-class covariance and minimizing the within-class covariance. However it involves matrix eigendecomposition which is computationally expensive in both time and memory requirement when the number of samples and the number of features are large. To deal with this complexity, we propose to use a recent dimension reduction method. The technique is based on fast approximate singular value decomposition (SVD) which has deep connections with low-rank approximation of the data matrix. The proposed approach, appSVD+LDA, consists of two stages. The first stage leads to a set of artificial features based on the original data. The second stage is the classical LDA. The foundation of our approach is presented and its performances in term of accuracy and computation time in comparison with some state-of-the-art techniques are provided for different real data sets.

Paper Citation

in Harvard Style

Elhadji Ille Gado N., Grall-Maës E. and Kharouf M. (2017). Linear Discriminant Analysis based on Fast Approximate SVD . In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 359-365. DOI: 10.5220/0006148603590365

in Bibtex Style

@conference{icpram17,
author={Nassara Elhadji Ille Gado and Edith Grall-Maës and Malika Kharouf},
title={Linear Discriminant Analysis based on Fast Approximate SVD},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2017},
pages={359-365},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006148603590365},
isbn={978-989-758-222-6},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Linear Discriminant Analysis based on Fast Approximate SVD
SN - 978-989-758-222-6
AU - Elhadji Ille Gado N.
AU - Grall-Maës E.
AU - Kharouf M.
PY - 2017
SP - 359
EP - 365
DO - 10.5220/0006148603590365