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A New Canonical Correlation Analysis Algorithm with Local Discrimination

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Abstract

In this paper, a new feature extraction algorithm is developed based on canonical correlation analysis (CCA), called Local Discrimination CCA (LDCCA). The method considers a combination of local properties and discrimination between different classes. Not only the correlations between sample pairs but also the correlations between samples and their local neighborhoods are taken into consideration in LDCCA. Effective class separation is achieved by maximizing local within-class correlations and minimizing local between-class correlations simultaneously. Besides, a kernel version of LDCCA (KLDCCA) is proposed to cope with nonlinear problems in experiments. The experimental results on an artificial dataset, multiple feature databases and face databases including ORL, Yale, AR validate the effectiveness of the proposed methods.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, 210016, Nanjing, China

    Yan Peng, Daoqiang Zhang & Jianchun Zhang

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  1. Yan Peng
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  2. Daoqiang Zhang
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  3. Jianchun Zhang
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Correspondence to Daoqiang Zhang.

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Peng, Y., Zhang, D. & Zhang, J. A New Canonical Correlation Analysis Algorithm with Local Discrimination. Neural Process Lett 31, 1–15 (2010). https://doi.org/10.1007/s11063-009-9123-3

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  • DOI: https://doi.org/10.1007/s11063-009-9123-3

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