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Feature Extraction Using Laplacian Maximum Margin Criterion

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Abstract

Feature extraction by Maximum Margin Criterion (MMC) can more efficiently calculate the discriminant vectors than LDA, by avoiding calculation of the inverse within-class scatter matrix. But MMC ignores the local structures of samples. In this paper, we develop a novel criterion to address this issue, namely Laplacian Maximum Margin Criterion (Laplacian MMC). We define the total Laplacian matrix, within-class Laplacian matrix and between-class Laplacian matrix by using the similar weight of samples to capture the scatter information. Laplacian MMC based feature extraction gets the discriminant vectors by maximizing the difference between between-class laplacian matrix and within-class laplacian matrix. Experiments on FERET and AR face databases show that Laplacian MMC works well.

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Authors and Affiliations

  1. School of Automation, Southeast University, Nanjing, 210096, China

    Wankou Yang & Changyin Sun

  2. Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China

    Helen S. Du

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

    Jingyu Yang

Authors
  1. Wankou Yang
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  2. Changyin Sun
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  3. Helen S. Du
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  4. Jingyu Yang
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Correspondence to Wankou Yang.

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Yang, W., Sun, C., Du, H.S. et al. Feature Extraction Using Laplacian Maximum Margin Criterion. Neural Process Lett 33, 99–110 (2011). https://doi.org/10.1007/s11063-010-9167-4

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