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Soft Computing
Published in: Soft Computing 10/2016

21-06-2015 | Methodologies and Application

Recursive locality preserving projection for feature extraction

Authors: Jie Xu, Shengli Xie

Published in: Soft Computing | Issue 10/2016

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Abstract

In this paper, we develop a novel feature extractor called recursive locality preserving projection (RLPP). RLPP inherits the advantages of LPP and at the same time makes some improvements. In RLPP, two local weight graphs are constructed. By combining the ideas of LPP and FLDA, a discriminative maximum criterion is proposed to make the local within-class data pairs close and between-class data pairs apart. To further improve the algorithm performance, a simple but effective method is presented to find the statistically uncorrelated discriminative vectors one by one. In this way, each new obtained discriminative vector not only maximizes the discriminative criterion but also contains minimum redundancy. Our experimental results on five databases demonstrate that RLPP is more powerful than the related methods.
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Metadata
Title
Recursive locality preserving projection for feature extraction
Authors
Jie Xu
Shengli Xie
Publication date
21-06-2015
Publisher
Springer Berlin Heidelberg
Published in
Soft Computing / Issue 10/2016
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-015-1745-y

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