LINEAR PROJECTION METHODS - An Experimental Study for Regression Problems

Carlos Pardo-Aguilar, José F. Diez-Pastor, Nicolás García-Pedrajas, Juan J. Rodríguez, César García-Osorio

2012

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.

Paper Citation

in Harvard Style

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 1: ICPRAM, ISBN 978-989-8425-98-0, pages 198-204. DOI: 10.5220/0003763301980204

in Bibtex Style

@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 1: ICPRAM,},
year={2012},
pages={198-204},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003763301980204},
isbn={978-989-8425-98-0},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 1: 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