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Recursive locality preserving projection for feature extraction

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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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This work was partially supported by the National Nature Science Foundation of China under Grant No. 61305036 and the China Postdoctoral Science Foundation funded project 2014M560657.

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Correspondence to Jie Xu.

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Communicated by V. Loia.

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Xu, J., Xie, S. Recursive locality preserving projection for feature extraction. Soft Comput 20, 4099–4109 (2016).

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