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Weighted linear embedding: utilizing local and nonlocal information sufficiently

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Abstract

Local discriminant embedding (LDE) only utilizes the local information and ignores the nonlocal information. Although linear discriminant analysis (LDA) utilizes the local information and the nonlocal information simultaneously, it treats these two kinds of information equally. As we know, the local information and the nonlocal information are both effective for feature extraction, but they have different roles in feature extraction. To utilize the local information and the nonlocal information simultaneously and utilize them distinctively, a new feature extraction approach called weighted linear embedding (WLE) is proposed by using the Gaussian weighting. Further, a method to set the optimal parameter of the Gaussian weighting is put forward. WLE is evaluated on YALE, FERET face databases, the PolyU palmprint database, and the PolyU finger-knuckle-print database. The experimental results demonstrate the effectiveness of WLE.

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Acknowledgments

This project is supported by the National Science Foundation of China under Grants No. 60632050, No. 60873151, No. 60973098, No. 60705006.

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Authors and Affiliations

  1. School of Computer Science and Technology, Nanjing University of Science and Technology, 210094, Nanjing, China

    Jun Yin, Jingbo Zhou, Zhong Jin & Jian Yang

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  1. Jun Yin
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  2. Jingbo Zhou
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  3. Zhong Jin
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  4. Jian Yang
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Correspondence to Jun Yin.

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Yin, J., Zhou, J., Jin, Z. et al. Weighted linear embedding: utilizing local and nonlocal information sufficiently. Neural Comput & Applic 21, 1845–1853 (2012). https://doi.org/10.1007/s00521-011-0528-3

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