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Authors: Carlos Pardo-Aguilar 1 ; José F. Diez-Pastor 1 ; Nicolás García-Pedrajas 2 ; Juan J. Rodríguez 1 and César García-Osorio 1

Affiliations: 1 University of Burgos, Spain ; 2 University of Córdoba, Spain

ISBN: 978-989-8425-98-0

Keyword(s): Localized sliced inverse regression, Linear discriminant analysis for regression, Weighted principal components analysis, Nonparametric discriminant regression analysis, Localized principal Hessian directions, Hybrid discriminant analysis for regression.

Related Ontology Subjects/Areas/Topics: Feature Selection and Extraction ; Pattern Recognition ; Regression ; Theory and Methods

Abstract: Two contexts may be considered, in which it is of interest to reduce the dimension of a data set. One of these arises when the intention is to mitigate the curse of dimensionality, when the data set will be used for training a data mining algorithm with a heavy computational load. The other is when one wishes to identify the data set attributes that have a stronger relation with either the class, if dealing with a classification problem, or the value to be predicted, if dealing with a regression problem. Recently, various linear regression projection models have been proposed that attempt to conserve those directions that show the highest correlation with the value to be predicted: Localized Slices Inverse Regression, Weighted Principal Component Analysis and Linear Discriminant Analysis for regression. However, the papers that have presented these methods use only a small number of data sets to validate their smooth functioning. In this research, a more exhaustive study is conducted using 30 data sets. Moreover, by applying the ideas behind these methods, a further three new methods are also presented and included in the comparative study; one of which is competitive with the methods recently proposed. (More)

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Paper citation in several formats:
Pardo-Aguilar, C.; F. Diez-Pastor, J.; García-Pedrajas, N.; J. Rodríguez, J. and García-Osorio, C. (2012). LINEAR PROJECTION METHODS - An Experimental Study for Regression Problems.In Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM, ISBN 978-989-8425-98-0, pages 198-204. DOI: 10.5220/0003763301980204

@conference{icpram12,
author={Carlos Pardo{-}Aguilar. and José F. Diez{-}Pastor. and Nicolás García{-}Pedrajas. and Juan J. Rodríguez. and César García{-}Osorio.},
title={LINEAR PROJECTION METHODS - An Experimental Study for Regression Problems},
booktitle={Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,},
year={2012},
pages={198-204},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003763301980204},
isbn={978-989-8425-98-0},
}

TY - CONF

JO - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,
TI - LINEAR PROJECTION METHODS - An Experimental Study for Regression Problems
SN - 978-989-8425-98-0
AU - Pardo-Aguilar, C.
AU - F. Diez-Pastor, J.
AU - García-Pedrajas, N.
AU - J. Rodríguez, J.
AU - García-Osorio, C.
PY - 2012
SP - 198
EP - 204
DO - 10.5220/0003763301980204

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