Improved Singular Value Decomposition for Supervised Learning in a High Dimensional Dataset

Ricco Rakotomalala; Faouzi Mhamdi

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Paper

Improved Singular Value Decomposition for Supervised Learning in a High Dimensional Dataset

In 6th International Workshop on Pattern Recognition in Information Systems - Volume 0ICEIS, 38-47, 2006 , Paphos, Cyprus

Authors: Ricco Rakotomalala ¹ and Faouzi Mhamdi ²

Affiliations: ¹ ERIC Laboratory - University of Lyon 2, France ; ² URPAH - University of Tunis, Tunisia

Abstract: Singular Value Decomposition (SVD) is a useful technique for dimensionality reduction with a controlled loss of information. This paper makes the very simple but worth-while observation that many attributes that contain no information about the class label, may thus be selected erroneously for a supervised learning task. We propose to first use a very tolerant filter to select on a univariate basis which attributes to include in the subsequent SVD. The features, “the latent variables”, extracted from relevant descriptors allow to build a better classifier with a significant improvement of the generalization error rate and less cpu time. We show the efficiency of this combination of feature selection and construction approaches on a protein classification context.

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Paper citation in several formats:

Rakotomalala, R. and Mhamdi, F. (2006). Improved Singular Value Decomposition for Supervised Learning in a High Dimensional Dataset. In 6th International Workshop on Pattern Recognition in Information Systems (ICEIS 2006) - PRIS; ISBN 978-972-8865-55-9, SciTePress, pages 38-47. DOI: 10.5220/0002472600380047

@conference{pris06,
author={Ricco Rakotomalala. and Faouzi Mhamdi.},
title={Improved Singular Value Decomposition for Supervised Learning in a High Dimensional Dataset},
booktitle={6th International Workshop on Pattern Recognition in Information Systems (ICEIS 2006) - PRIS},
year={2006},
pages={38-47},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002472600380047},
isbn={978-972-8865-55-9},
}

TY - CONF

JO - 6th International Workshop on Pattern Recognition in Information Systems (ICEIS 2006) - PRIS
TI - Improved Singular Value Decomposition for Supervised Learning in a High Dimensional Dataset
SN - 978-972-8865-55-9
AU - Rakotomalala, R.
AU - Mhamdi, F.
PY - 2006
SP - 38
EP - 47
DO - 10.5220/0002472600380047
PB - SciTePress