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Erschienen in: Soft Computing 10/2016

21.06.2015 | Methodologies and Application

Recursive locality preserving projection for feature extraction

verfasst von: Jie Xu, Shengli Xie

Erschienen in: Soft Computing | Ausgabe 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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Metadaten
Titel
Recursive locality preserving projection for feature extraction
verfasst von
Jie Xu
Shengli Xie
Publikationsdatum
21.06.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 10/2016
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-015-1745-y

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