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Feature Extraction Based on Maximum Nearest Subspace Margin Criterion

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Abstract

Based on the classification rule of sparse representation-based classification (SRC) and linear regression classification (LRC), we propose the maximum nearest subspace margin criterion for feature extraction. The proposed method can be seen as a preprocessing step of SRC and LRC. By maximizing the inter-class reconstruction error and minimizing the intra-class reconstruction error simultaneously, the proposed method significantly improves the performances of SRC and LRC. Compared with linear discriminant analysis, the proposed method avoids the small sample size problem and can extract more features. Moreover, we extend LRC to overcome the potential singular problem. The experimental results on the extended Yale B (YALE-B), AR, PolyU finger knuckle print and the CENPARMI handwritten numeral databases demonstrate the effectiveness of the proposed method.

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Authors and Affiliations

  1. School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, 210094, People’s Republic of China

    Yi Chen & Zhong Jin

  2. School of Information Engineering, Jiangxi Manufacturing Technology College, Nanchang, 330095, China

    Zhenzhen Li

Authors
  1. Yi Chen
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  2. Zhenzhen Li
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  3. Zhong Jin
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Correspondence to Yi Chen.

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Chen, Y., Li, Z. & Jin, Z. Feature Extraction Based on Maximum Nearest Subspace Margin Criterion. Neural Process Lett 37, 355–375 (2013). https://doi.org/10.1007/s11063-012-9252-y

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  • DOI: https://doi.org/10.1007/s11063-012-9252-y

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